Enhanced systems, apparatus, and methods for improved automated and autonomous operation of logistics ground support equipment

ABSTRACT

An improved retrofit assembly for ground support equipment providing autonomous operation of the logistics ground support equipment (GSE). The assembly has a refit control system attached to the GSE (with a vehicle dynamics control processor and a data preprocessing control processor), retrofitted proprioceptive sensors coupled to the vehicle dynamics control processor monitoring operating parameters and characteristics of the GSE, retrofitted exteroceptive sensors coupled to the data preprocessing control processor that monitor an exterior environment of the GSE, and refit actuators for different control elements on the GSE and to autonomously alter motion of the GSE. The refit control system programmatically receives sensor data from the proprioceptive sensors and exteroceptive sensors; optimizes a path for the GSE based upon the different sensor data; and activates at least one of the actuators according to the optimized path for the GSE.

PRIORITY APPLICATION

The present application hereby claims the benefit of priority to relatedU.S. Provisional Patent Application No. 62/711,691 and entitled“Enhanced Systems, Apparatus, and Methods for Improved Automated andAutonomous Operation of Logistics Ground Support Equipment.”

FIELD OF THE DISCLOSURE

The present disclosure generally relates to systems, apparatus, andmethods in the field of logistics ground support equipment and, moreparticularly, to various aspects of systems, apparatus, and methodsrelated to improved automated and/or autonomous aspects of operationsfor logistics ground support equipment, such as a cargo tractor andassociated dollies towed by the cargo tractor (or tug).

BACKGROUND

There is a need for a technical solution that may be deployed to enhanceways to retrofit or refit existing logistics ground support equipment(referred to herein as GSE) for enhanced autonomous operation in waysthat help to avoid collisions causing damage to logistics vehicles (suchas cargo tractors and associated dollies) and doing so in an enhancedmanner that improves system performance and helps reduce falsepositives.

In particular, what is described are various exemplary types ofdelineation methods and systems where an industrial vehicle may beretrofit or refit with an assembly to provide a level of autonomousoperation for the vehicle in a logistics environment where cargo may betransported using GSE (e.g., cargo tractor or tug, one or dollies, andthe like) in an air operations environment that is busy and crowded withobjects located through the operations environment that should beavoided and under conditions where operation of the GSE can lead tocollisions and accidents.

What is needed are systems and assemblies of components that may beretrofit or refit onto existing GSE so as to provide advantageous fullyor semi-autonomous operation of the GSE that helps avoid collisions andhelps to efficiently operate the GSE within such an air operationsenvironment filled with hazardous conditions, obstacles, and restrictedspace.

SUMMARY

In the following description, certain aspects and embodiments willbecome evident. It should be understood that the aspects andembodiments, in their broadest sense, could be practiced without havingone or more features of these aspects and embodiments. It should beunderstood that these aspects and embodiments are merely exemplary.

In general, an aspect of the disclosure relates retrofitting a cargo tugcontrol system with control actuators, sensors and usage dataacquisition. This may enable semi-autonomous movement of the tug (a typeof logistics ground support equipment) in airport logistics environment,captures usage data, and allows for enhanced maintenance monitoring forsuch tugs. In more detail in this aspect, an exemplary retrofit assemblyapparatus is described for use on a logistics ground support equipmentto enhance a level of autonomous operation of the logistics groundsupport equipment. Such an assembly generally includes a refit controlsystem, retrofitted sensors, and actuators. The refit control system isattached to the logistics ground support equipment, and has a vehicledynamics control processor and a data preprocessing control processor.The retrofitted sensors are coupled to the refit control system anddisposed on the logistics ground support equipment as retrofittedequipment to the logistics ground support equipment. Such retrofittedsensors include a first group of proprioceptive sensors coupled to thevehicle dynamics control processor that monitor operating parameters andcharacteristics of the logistics ground support equipment, and a secondgroup of exteroceptive sensors coupled to the data preprocessing controlprocessor that monitor an exterior environment of the logistics groundsupport equipment. The actuators are coupled to the refit controlsystem, where each of the actuators is disposed on the logistics groundsupport equipment as a retrofit actuator to add control of respectivecontrol elements on the logistics ground support equipment (e.g.,steering, throttle, transmission, braking, and the like) so as toautonomously alter motion of the logistics ground support equipmentduring operation of the assembly. The refit control system isprogrammatically configured to be operative to receive first sensor datagenerated by the first group of proprioceptive sensors using the vehicledynamics control processor of the refit control system; receive secondsensor data generated by the second group of exteroceptive sensors usingthe data preprocessing control processor; optimize a path (e.g., via adetermined heading and/or speed) for the logistics ground supportequipment based upon the second sensor data from the data preprocessingcontrol processor and the first sensor data from the vehicle dynamicscontrol processor; and activate, by the vehicle dynamics controlprocessor, at least one of the actuators according to the optimized pathfor the logistics ground support equipment.

Another aspect of the disclosure focuses on an adaptive control systemfeedback loop monitoring system for improved engine performance. Thismay help by proactively addressing latency and response time forlogistics ground support equipment, such as a cargo tug, withconsiderations of sensitivity to load and/or sensitivity to position(e.g., on ramp, detected obstacles, location & orientation (nearplane→lower speed), building transitions, weight/grade) to improveoperations of the equipment.

In still another aspect of the disclosure, methods and systems maydeploy enhanced data preprocessing and spatial awareness techniques viathe use of the known contextual environment for particular logisticsground support equipment (such as a cargo tug). Such a contextualenvironment may, for example, include information on known environments(e.g., buildings, airport layout, plane footprint, and the like) as wellas a temporally detected environment of an airport and loading areas(e.g., temporary barriers/temporary objects vs base layout). Furtheraspects of the contextual environment may include a time component addedto static/known layout issue (e.g., day v. night; seasonal issues;personnel detected) when navigating the logistics areas, such as anairport and/or particular loading and unloading areas.

Another aspect of the disclosure includes a hive type of management fora fleet of logistics ground support equipment (such as a fleet of cargotugs). This may involve the creation of temporal spatial awareness datathat is proactively distributed to other autonomous vehicles in anairport fleet so that different ones of the autonomous vehicles in thefleet may be aware of operations of other members of the fleet as a wayof avoiding fleet member collisions.

In yet another aspect of the disclosure, further methods and systemsinvolve automatic deactivation of autonomous control of logistics groundsupport equipment. This may involve the detection of the presence of ahuman operator and identifying purposeful contact in contrast toaccidental contact with other objects (e.g., hooking up to a dolly as atype of anticipated object vs. an unintended collision with anunanticipated object), and human feedback upon deactivation.

Additional advantages of these and other aspects of the disclosedembodiments and examples will be set forth in part in the descriptionwhich follows, and in part will be evident from the description, or maybe learned by practice of the invention. It is to be understood thatboth the foregoing general description and the following detaileddescription are exemplary and explanatory only and are not restrictiveof the invention as recited in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments according toone or more principles of the invention and together with thedescription, serve to explain one or more principles of the invention.In the drawings,

FIG. 1A is a diagram of an exemplary operating environment for exemplarylogistics ground support equipment that uses a retrofit assemblyapparatus for enhanced autonomous operation in accordance with anembodiment of the invention;

FIG. 1B is a diagram illustrating further details of the exemplarylogistics ground support equipment that uses a retrofit assemblyapparatus for enhanced autonomous operation as shown in FIG. 1A inaccordance with an embodiment of the invention;

FIG. 2 is exemplary high level function block diagram for an exemplaryretrofit assembly apparatus used on logistics ground support equipmentfor enhanced autonomous operation in accordance with an embodiment ofthe invention;

FIG. 3 is a more detailed diagram of an exemplary retrofit assemblyapparatus used on logistics ground support equipment for enhancedautonomous operation shown with logical segmentation of differentelements and roles within the apparatus in accordance with an embodimentof the invention;

FIG. 4 is a diagram of exemplary implementation details for parts of anexemplary retrofit assembly apparatus used on logistics ground supportequipment for enhanced autonomous operation in accordance with anembodiment of the invention;

FIG. 5 is an exemplary table with information on different exemplarysensors in accordance with an embodiment of the invention;

FIG. 6 is an exemplary table with information on exemplary sensor tasksfor a combination of exemplary sensors in accordance with an embodimentof the invention;

FIG. 7 is an exemplary table with information on implementation examplesfor components of an exemplary refit control system in accordance withan embodiment of the invention;

FIG. 8 is diagram illustrating the use of zones around an exemplarybeacon device that may be used as a reference point type ofenvironmental object being detected by an exemplary retrofit assemblyapparatus used on logistics ground support equipment for enhancedautonomous operation in accordance with an embodiment of the invention;

FIG. 9 is an exemplary framework diagram of a dynamic modeling frameworkthat determines the instantaneous status of an exemplary cargotractor-dolly system in accordance with an embodiment of the invention;

FIG. 10 is a diagram of an exemplary collision envelope for a singlecargo tractor type of logistics ground support equipment model inaccordance with an embodiment of the invention;

FIG. 11 is a diagram of an exemplary collision envelope for a singledolly type of ground support equipment model in accordance with anembodiment of the invention;

FIG. 12 is a diagram of exemplary collision awareness for a train ofcargo dollies in accordance with an embodiment of the invention;

FIG. 13 is a diagram of an exemplary search tree for predictive controlusing three look-ahead steps in accordance with an embodiment of theinvention;

FIG. 14 is a flow diagram of an exemplary method for improved engineperformance using an adaptive control system on a logistics groundsupport equipment in accordance with an embodiment of the invention; and

FIG. 15 is a diagram of an exemplary fleet of logistics ground supportequipment involving an exemplary central server that may manage,collect, and distribute relevant contextual environment data to and fromlogistics ground support equipment in the fleet in accordance with anembodiment of the invention.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to various exemplary embodiments asdescribed below in conjunction with the embedded drawings and tabularinformation. Those skilled in the art will appreciate that differentembodiments may implement a particular part in different ways accordingto the needs of the intended deployment and operating environment forthe respective embodiments.

The following describes various embodiments of different systems,apparatus, and applied methods that are deployed and used with improvedand enhanced operations of logistics ground support equipment, such as acargo tractor and associated dollies. Various aspects of differentembodiments may include, for example, retrofitting or refitting a cargotug control system with control actuators, sensors and usage dataacquisition. This may enable semi-autonomous movement of the cargotractor or tug (a type of logistics ground support equipment) in airportlogistics environment, captures usage data, and allows for enhancedmaintenance monitoring for such tugs. Another embodiment may focus on anexemplary adaptive control system feedback loop monitoring system forimproved engine performance. This may help by proactively addressinglatency and response time for logistics ground support equipment, suchas a cargo tug, with considerations of sensitivity to load and/orsensitivity to position (e.g., on ramp, detected obstacles, location &orientation (near plane lower speed), building transitions,weight/grade) to improve operations of the equipment. Still furtherembodiments may have methods and systems deploying enhanced datapreprocessing and spatial awareness techniques via the use of the knowncontextual environment for particular logistics ground support equipment(such as a cargo tug). Such a contextual environment may, for example,include information on known environments (e.g., buildings, airportlayout, plane footprint, and the like) as well as a temporally detectedenvironment of an airport and loading areas (e.g., temporarybarriers/temporary objects vs base layout). Further aspects of thecontextual environment may include a time component added tostatic/known layout issue (e.g., day v. night; seasonal issues;personnel detected) when navigating the logistics areas, such as anairport and/or particular loading and unloading areas.

Additional embodiments may deploy a hive type of management for a fleetof logistics ground support equipment (such as a fleet of cargo tugs).This may involve the creation of temporal spatial awareness data that isproactively distributed to other autonomous vehicles in an airport fleetso that different ones of the autonomous vehicles in the fleet may beaware of operations of other members of the fleet as a way of avoidingfleet member collisions.

Further method and system embodiments may involve automatic deactivationof autonomous control of logistics ground support equipment. This mayinvolve the detection of the presence of a human operator andidentifying purposeful contact in contrast to accidental contact withother objects (e.g., hooking up to a dolly as a type of anticipatedobject vs. an unintended collision with an unanticipated object), andhuman feedback upon deactivation.

And still additional method and system embodiments provide pathoptimization solutions that may leverage retrofitted hardware andsensors as a package deployed on existing fleet machines and groundsupport equipment. Such a solution may further leverage environmentalawareness through data provided to the enhanced ground supportequipment, where the environmental awareness takes the form ofcontextual data on temporary objects/landmarks, permanent or fixedobjects/landmarks, and geofencing type of boundary information as wellas information on aircraft being loaded or unloaded (e.g., type ofaircraft, changes to the aircraft, the current loaded layout or statusfor the aircraft, what may be desired to be loaded onto the aircraft).

GSE Autonomy Refit (Cargo Tractor Autonomony Refit or C-TAR)

Retrofitting logistics ground support equipment (GSE) so that it mayprovide semi- or fully-autonomous operations is explained with a varietyof the embodiments described herein. Embodiments involving refit GSE mayalso be referenced throughout this description using the term “C-TAR”(Cargo Tractor Autonomy Refit) as an electrical, mechanical, andsoftware design package that may convert a standard, human driven cargotractor (e.g., an exemplary logistics ground support equipment or GSE),into a semi- or fully-autonomous airport vehicle capable of alteringmovement of the tractor so as to, for example, avoid contact withobjects including fixed (such as facilities and components), static(such as parked vehicles or aircraft), and dynamic (such as movingvehicles or humans). Embodiments of C-TAR may also allow cargo tractors(as types of enhanced GSE) to be capable of being driven either byremote or autonomously based on AI (artificial intelligence) logic thatinterprets the local environment and surroundings based on predeterminedsafety and process driven requirements specific to the logisticsindustry and airport environment.

Different embodiments may include, for example, retrofitting orrefitting a cargo tug control system with control actuators, sensors andusage data acquisition. This may enable semi-autonomous movement of thecargo tractor or tug (a type of logistics ground support equipment)according to an optimized path in airport logistics environment,captures usage data, and allows for enhanced maintenance monitoring forsuch tugs.

FIG. 1A is a diagram of an exemplary operating environment for exemplarylogistics ground support equipment that may use an exemplary retrofitassembly apparatus for enhanced autonomous operation in accordance withan embodiment of the invention. As shown in FIG. 1A, the operatingenvironment includes an exemplary aircraft 100 as a type of high valueasset. In general, a high value asset may be considered to be certainequipment, structure, and/or people where mobile industrial vehicles(such as cargo tractor 115 and its linked dollies 120) are desired tohave limitations when approaching or moving around or near such assets.Indeed, certain areas may be considered a high value asset for thepotential of having such equipment, structure, and/or people but do notnecessarily have to be currently occupied. In various embodimentsdescribed herein, an off limit (or restricted movement) area associatedwith such a high value asset may be established or determined as aboundary for the protection of that high value asset.

The exemplary aircraft 100, shown from above in FIG. 1A, may be a typeof high value asset used to transport items as part of a logisticspickup and/or delivery operation. As shown in FIG. 1A, the exemplaryaircraft 100 has a nose cone structure protruding from the front end ofthe aircraft 100, protruding engines on each wing, and tail structurethat protrudes from the back end of the aircraft 100. Those skilled inthe art will appreciate that such protrusions are examples of points onthe aircraft that are of more risk for collisions with mobile logisticsground support equipment operating in the vicinity of the aircraft 100.In the example shown in FIG. 1A, an exemplary reflective beacon (e.g.,105 a-105 d) may be placed adjacent to each of such protrusions and usedduring operation of the exemplary refit logistics ground supportequipment described herein. Such an exemplary reflective beacon 105a-105 d may be implemented with passive reflectors for LiDAR or radar toenhance the ability to locate and detect such beacons as locators of keylocations around aircraft 100, even in poor visibility. As discussed inmore detail below, a model predictive control module operating on avehicle dynamics processor (as part of the retrofit control system of anembodiment) may use range and bearing to such beacons 105 a-105 d aspart of governing speed and cargo tractor 115 motion.

In still another example, an embodiment may use a vehicle dynamics modeland predictive calculations relative to a cargo tractor 115 and itstowed vehicles 120 (e.g., dollies/trailers) to help prevent collisionsby the cargo tractor 115 as well as the associated towed dollies 120even though there is no active detection mechanism deployed on thedollies. The vehicle dynamics model (shown in FIG. 3), in general, isused to inform calculations performed on processing systems deployed onthe cargo tractor 115 of possible future states of the cargo tractor 115and dollies 120 (i.e., the powered logistics ground support equipment aswell as the linked towed vehicles that follow). Thus, when using such amodel and calculations as part of an exemplary system, a virtualperimeter 130 along the sides of the dolly train may effectively boundthe system to prevent collisions from off-tracking the associated toweddollies 120. As shown in FIG. 1A, the widening track outline ofperimeter 130 indicates a probabilistically determined dolly locationthat can be used by an exemplary collision avoidance system without theneed for actual location sensors to be deployed on the dollies 120.

FIG. 1A also illustrates a cargo loading structure 110 shown alongsidethe fuselage of the exemplary aircraft 100 where items (e.g., unpackagedgoods, packaged goods, and containers that may be used for transportinggoods) may be loaded into the aircraft 100 from a cargo tractor 115 andits associated dollies 120 or where such items may be off loaded fromthe aircraft 100 to the cargo tractor 115 and its associated dollies 120as part of different logistics operations. In general, the cargo tractor115 may move along a path 125 near the aircraft 100 and its cargoloading structure 115 so as to facilitate pickup and/or delivery ofitems from its associated dollies 120 to and from the aircraft 100. Theconcentric rings around beacons 105 a-105 d in FIG. 1A indicate sensedreflective beacons 105 a-105 d near the high value asset (e.g., thenoted protrusions on the aircraft) and protected or restricted areassurrounding each beach (e.g., area 106 restricted for no entry, area 107restricted for slow speed upon entering such an area). Using range andbearing to beacons 105 a-105 d, an onboard control system may issuesignals to control motion and speed when the virtual perimeter 130 isinside such control zones (e.g., area 106, area 107). Missing beaconsoutside of the sensor scanning range 108 of the sensor suite on boardthe cargo tractor 108 are not tracked by the sensor suite. As discussedin more detail below, cargo tractor 115 may deploy sensors (e.g.,exteroceptive sensors) to sense environmental objects external to thecargo tractor, such as along the path of travel 135 for cargo tractor115 and including beacons 105 a-105 d.

Those skilled in the art will appreciate that areas of technologyvariance in an exemplary cargo tractor fleet include (but are notlimited to) electronic control and drive-by-wire systems, and powertraintechnology (ranging from carbureted engines to modern fuel-injectingengines). Some exemplary cargo tractors (as exemplary types of logisticsground support equipment) may include hydraulic versus mechanicalsteering, throttle by wire versus throttle by cable, and vacuum boosterversus hydraulic booster braking. In some embodiments, older model cargotractors may require more work to make autonomous as entire systems mayneed to be updated to support any level of autonomous control.Additionally, those skilled in the art will appreciate that performanceof refitted GSE (such as cargo tractors or tugs) may vary depending onthe original technology within the vehicle.

Those skilled in the art will appreciate that drive-by-wire is commonlyconsidered the electronic system found in vehicles replacing hydrauliclinkages. Maintaining speed via adaptive cruise control and overridingthe driver for active collision avoidance systems requires electroniccontrol of throttle, brakes, shifting, and steering. This electroniccontrol is a necessary component of vehicle safety and collisionavoidance systems. Embodiments of autonomous cargo tractors use thiselectronic control system as a retrofit type of control system that maysent critical electronic signals to vehicle systems such as the engineand braking—two systems normally controlled by human hand interactionwith the steering wheel and foot interaction with pedals. In order toassure accurate electronic response, such a retrofit control system usesfeedback, such as the actual speed of the vehicle, the actual andprecise steering angle, and current engine status all of which can beprovided by sensors. An embodiment may implement a vehicle dynamicscontrol computer (e.g., as a control processor) and a data preprocessingcontrol computer (e.g., as another control processor) as part of such aretrofit control system to control actuators and sensors which in turncontrol independent vehicle systems to meet vector requirements definedby a given heading and speed.

FIG. 1B is a diagram illustrating further details of components ofexemplary logistics ground support equipment (e.g., exemplary cargotractor 115) configured with an exemplary retrofit assembly apparatusfor enhanced autonomous operation in accordance with an embodiment ofthe invention. Exemplary cargo tractor 115 is illustrated with variouscontrol elements of the tractor 115 as well as with various control,sensing, and actuation components of an exemplary retrofit assembly thatmay be used to refit tractor 115 so as to allow for and enhance a levelof autonomous operation of tractor 115. Referring now to FIG. 1B, theblock diagram portion illustrating exemplary cargo tractor 115 includeswheels 150, an engine 142, throttle 154, fuel system 152, transmissionsystem 146, steering system 144, and brake system 148. The exemplaryretrofit assembly shown in FIG. 1B is based around an exemplary refitcontrol system or module 140 attached to tractor 115 and which may beimplemented with vehicle dynamics control processor and a datapreprocessing control processor based systems.

Exemplary internal or proprioceptive sensors of the exemplary retrofitassembly shown in FIG. 1B include an operator detection sensor 160 (alsoreferred to as a driver present sensor), fuel level sensor 162, wheelspeed sensor 164, steering angle sensor 184, transmission status sensor166, brake sensor 168, throttle position sensor 176, engine crank/runstatus sensor 178, engine RPM sensor 180, temperature sensor 194, andweight sensor 196. Exemplary fuel level sensor 162 generates sensor datareflecting a detected fuel level in fuel system 152. Exemplary wheelspeed sensor 164 generates sensor data based upon monitoring rotationalspeed of wheel 150 relative to tractor 115. Exemplary steering anglesensor 184 generates sensor data based upon monitoring an angle at whicha steering wheel on tractor 115 (part of the steering system 144) hasbeen turned. Exemplary engine crank/run status sensor 178 generatessensor data based upon monitoring whether the engine 142 has beencranked or activated and is running. Exemplary throttle position sensor176 generates sensor data based upon monitoring a position of throttle154 disposed on the tractor 115 and coupled to engine 142. Exemplarybrake status sensor 168 generates sensor data based upon monitoringmotion of a brake pedal (e.g., a part of brake system 148) disposed ontractor 115. Exemplary engine RPM sensor 180 (also referred to as anengine revolution sensor) generates sensor data based upon monitoringengine revolutions of engine 115 disposed on the tractor 115 and isindicative of the current power produced by engine 142. Exemplaryoperator detection sensor 160 generates sensor data based uponmonitoring at least one of an operators presence on the tractor 115(e.g., detected weight on an operator's seat), the operator's ingressinto tractor 115 (e.g., breaking a beam at an ingress point on tractor115), and the driver's egress from the logistics ground supportequipment (e.g., breaking a beam at an egress point on tractor 115).Exemplary transmission status sensor 166 generates sensor data basedupon monitoring a status of transmission system 146 (e.g., the state ofgear for the transmission). Exemplary temperature sensor 194 monitors atemperature related to the tractor 115 (e.g., an ambient temperature onor near tractor 115). Exemplary weight sensor 196 monitors a weight ofcargo being transported by tractor 115 (e.g., a weight bearing on atowing receiver on tractor 115, a weight of cargo as disposed on anydolly being pulled by tractor 115, and the like). These proprioceptivesensors are each coupled to the retrofit assembly's control system 140where respective sensor data generated may be used by processingcircuitry on control system 140.

Exemplary exteroceptive sensors of the exemplary retrofit assembly shownin FIG. 1B include exemplary inertial measurement unit (IMU) 156, GPSreceiver 158, LiDAR 186, camera 188, and radar 190. These exteroceptivesensors are also each coupled to the retrofit assembly's control system140 where respective sensor data generated may be used by processingcircuitry on control system 140.

Exemplary actuators shown in exemplary retrofit assembly of FIG. 1Binclude exemplary throttle actuator 174, brake actuator 172,transmission actuator 170, steering actuator 182, and engine actuator192. Exemplary throttle actuator 174 responsively controls throttle 154to cause a change in speed of tractor 115 according to control signalsfrom control system 140. Exemplary brake actuator 172 responsivelycontrols brake 148 (e.g., a brake pedal or brake pad of system 148) tocause the brake 148 to decrease the motion of the tractor 115 accordingto control signals from control system 140. Exemplary transmissionactuator 170 responsively controls transmission system 146 to cause achange in gear selection for the transmission system 146 that alters themotion of the tractor 115 according to control signals from controlsystem 140. Exemplary steering actuator 182 responsively controlssteering system 144 to cause a change in position for a steering wheelof the steering system 144 that alters a direction of movement for thetractor 115 according to control signals from control system 140.Exemplary engine actuator 192 responsively activates and deactivatesengine 142 on tractor 115 (e.g., starts the engine to allow for movementof tractor 115 or shuts down the engine to prevent further movement oftractor 115) according to control signals from control system 140.

FIG. 2 is exemplary high-level function block diagram for an exemplaryretrofit assembly apparatus or system used on logistics ground supportequipment for enhanced autonomous operation in accordance with anembodiment of the invention. Referring now to FIG. 2, an embodiment ofan exemplary refit or retrofit assembly 200 comprises three mainparts: 1) hardware components, such as the vehicle itself, sensors, andcomputer hardware, 2) a vision subsystem, composed of video dataprocessing and range data processing, and 3) a reasoning subsystem. Eachof these three integrated systems are represented in FIG. 2 via theSensors and Actuators 205, the Data Processing 210, and the ControlAlgorithms 215 respectively.

As shown above in FIGS. 1B and 2, an embodiment may deploy and usesensors that are proprioceptive sensors 220 and exteroceptive sensors230. In general, exemplary proprioceptive sensors 220 areself-monitoring sensors that are responsible for monitoringself-maintenance and controlling internal status. These sensors 220 aretypically found in robots and other autonomous equipment where thesignal is used for measuring a specified component of the system. Thesignal, or transmitted sensor data, is then used by the autonomousequipment to monitor such systems as battery or fuel level, heat and/orgeneral temperature of critical areas and surroundings, and location anddirectional vectors of moving components. The proprioceptive sensors 220referenced in FIG. 2 (where examples are used as components of anexemplary retrofit assembly apparatus for tractor 115 to provide andenhance a level of autonomous operation of tractor 115) include sensorsthat monitor some of the areas an exemplary C-TAR system embodiment,which then may be used as data considered in relation to AI decisionmaking, as explained in more detail below:

Wheel speed—The rotational speed at which a tire/wheel 150 movesrelative to the vehicle 115 will be measured in order to determineactual vehicle speed relative to the ground.

Steering angle—Shaft or rotary encoders are a type of device that couldbe used to provide the angle at which the steering wheel of steeringsystem 144 has been turned. Rotary encoders are electro-magnetic devicesthat work as a transducer to convert the angular position of a shaft ofaxle to an analog or digital code. This angle provides feedback on theturn angle the cargo tractor 115 is expected to make.

Engine run status—Sensors 178 will provide feedback from the engine 142regarding whether or not it is running or active.

Throttle position—A throttle position sensor 176 or TPS is used tomonitor the throttle position of tractor 115 and is usually located on abutterfly spindle/shaft. A TPS sensor directly monitors position onvehicles that do not utilize drive-by-wire systems. For the cargotractors that do utilize drive-by-wire, an embodiment may use anelectronic throttle control or ETC to provide position data in afeedback loop as the sensor data generated by sensor 176.

Brake status—Brake pedal position sensors 168 are electromechanicalsensors that detect motion of a brake pedal (or other part of brakesystem 148). That motion and sensor feedback will likely vary based onthe hydraulic brake configuration and age of the cargo tractor beingretrofit and so sensors on the individual brake pads may be required insome embodiments. The force needed to create the same amount of brakepad friction will likely vary between cargo tractors.

Engine revolutions per minute (rpms)—The speed at which the engine 142is moving is feedback that may be captured using an effect sensor (e.g.,RPM sensor 180) that detects magnetism generated by the voltage throughthe sparkplug. Using a proximity sensor (e.g., another exemplaryimplementation of RPM sensor 180) to detect the speed of a rotatingpiece of machinery off of the engine 142 is another possible solution togauge rpms and speed of tractor 115.

Driver present—Presence detection sensors (e.g., operator detectionsensor 160) that can be placed on or in the seat of cargo tractor 115 todetermine a driver's ingress and egress from the vehicle 115. Thesesensors can also be utilized to determine when passengers are present.

Fuel level—Ultrasonic and/or capacitive fuel level sensors (e.g.,exemplary implementations of fuel level sensor 162) can be integratedwith the fuel tank 152 in order to capture information on all fuel typessuch as gas, diesel, and other bio fuels that may be put into the cargotractor 115.

Transmission status—Transmission control shift timing, transmissioninput speed, transmission output speed, and other data created by thetransmission and powertrain in transmission system 146 are variablesthat will be captured via sensor 166 in order to relay information backto the C-TAR unit (e.g., retrofit control module 140). In modernvehicles at least, an automatic transmission control unit or TCU mayreceive electronic data from all of the associated transmission sensorsand uses that information to determine how to calculate when the changeof gears should occur for things like optimum performance and fueleconomy. For cargo tractor 115, this type of data may be gathered usingsensor 166 into the C-TAR control module 140 for decision makingpurposes either through tapping into existing transmission sensors orthrough the addition of more electronic sensors on important movementparts within the drivetrain/transmission system 146.

In addition to such exemplary internal sensors used to gather feedbackand data regarding the control functions of the cargo tractor 115(referenced as proprioceptive sensors 220 in FIG. 2), additionalexternal sensors (referenced as exteroceptive sensors 230) retrofittedto tractor 115 via the C-TAR package gather data on the surroundingenvironment. The exemplary list provided here represents some of thepotential sensors that could be used in an embodiment.

Camera—An embodiment may use a single or multiple cameras (e.g., such asexemplary camera 188) for forward obstacle and vehicle detection, lanedetection, and traffic sign recognition (either at standard sign heightor presented on the road itself such as a stop sign on the tarmac).Exemplary cameras, such as camera 188, may be mounted at multiple pointsin the front of the cargo tractor 115 and behind the body for sidewayviews, parking and backing up, as well as traffic intersectiondetection. The cameras themselves may acquire images in a free-runningmode with the situation determining whether or not images and datashould be stored internally/externally for AI processing. An embodimentmay use Firewire, USB 3, or some other high-speed data bus to create aninterface between the cameras and any onboard vision system that is partof control module 140. Stereo algorithms may be used to reconstruct the3D environments and provide information about the immediate surroundingseither to the primary refit cargo tractor 115 or to any other cargotractors existing on the autonomous vehicle network (e.g., otherlogistics ground support equipment in wireless communication withtractor 115 via a wireless transceiver in module 140).

Radar—Radar, such as mm-wave scanning radar technology, may be deployedusing a radar sensor (e.g., exemplary radar 190) in an embodiment as anoption for collision avoidance especially when the environment isobscured with smoke, dust, and weather. Functioning without issue duringall potential weather and obstruction type events is desired for theC-TAR and the refit cargo tractor 115. As with mainstream vehicles,radar systems could be installed on the front and back of the cargotractor 115 warning the AI (e.g., the control systems in module 140) ora human driver of impending impact via feedback on a user interface(e.g., visual information on a display on a dashboard of tractor 115,audible information through a speaker on tractor 115, and the like) orby actually engaging the brakes 148. Other use cases for exemplary radarsensors 190 include creating an adaptive cruise control that alwaystakes into account the movement of other vehicles around the cargotractor 115.

LiDAR—LiDAR is similar to radar with the difference being that this typeof sensor uses laser detection rather than radio. A LiDAR sensor (e.g.,exemplary LiDAR sensor 186) may be used on an embodiment of refit cargotractor 115 for obstacle detection and avoidance to navigate safelythrough environments. LiDAR sensors could be used to create cost maps orpoint cloud outputs (e.g., multi-dimensional map data) that providesdata for software running on control module 140 to determine wherepotential obstacles exist in the environment relative to tractor 115 andwhere the semi- or fully autonomous cargo tractor 115 is in relation tothose potential obstacles. LiDAR could also be used for adaptive cruisecontrol.

IMU (Inertial Measurement Unit)—IMUs (e.g., exemplary IMU 156) may beused to measure and report on a vehicle's location, velocity,orientation, and gravitational forces when deployed as part of a refitGSE (i.e., a logistics ground support equipment with an exemplaryretrofit assembly apparatus to provide and enhance autonomous operationof the GSE). In one embodiment, the application for ground autonomycould be such that the integrated accelerometers, gyroscopes, andmagnetometers within the IMU 156 may be used in place of GPS 158 when astrong communication signal is not available (such as within tunnels,buildings, and when electronic interference is present). Those skilledin the art will appreciate that an IMU (such as IMU 156) may allowcomputing systems in module 140 to track the cargo tractor's positionusing a method called dead reckoning. Given that the cargo tractor 115(or other GSE) refit with C-TAR may spend large portions of theiroperational time under large craft, inside buildings, hidden bysignificant weather, and around a high amount of wireless communicationtraffic such as 802.11, Bluetooth, and cellular, embodiments may deployan IMU 156 to aid in keeping exact positioning.

GPS (Global Positioning System)—For the times when an embodiment ofrefit cargo tractor 115 is out in the open and not impeded byobstruction, electronic noise, weather and other communication limitingenvironmental factors, exemplary GPS 158 may be used by refit cargotractor 115 to provide location data representing current positioning ofthe tractor 115. Those skilled in the art will appreciate that GPS 158on a semi- or fully autonomous cargo tractor 115 may use real timegeographical data received from several GPS satellites to calculatelongitude, latitude, speed, and course. In an embodiment, routes couldbe preprogrammed into the C-TAR (e.g., refit control system module 140)so that known coordinates could be utilized without human control or toassist human control by making virtual barriers in certain locationsimproving safety and decreasing damage to the tug or its environment. Infurther embodiments, GPS 158 could be used to track all movements ofhuman drivers/operators or provide optimal routes based on processrequirements, time of day, special sort constraints, or any otherairport/logistics regulation.

FIGS. 5 and 6 provide information on exemplary evaluations of sensorsconsidered for use in various embodiments. All sensors should beconsidered as options for performing collision avoidance detection andenvironmental data collection in embodiments of the invention. Exemplarytable 500 shown in FIG. 5 provides information on different exemplarysensors in accordance with an embodiment of the invention where theinformation in table 500 provides examples of different types of passiveand active sensors and an exemplary comparison of sensing tasks,compatibility, integrations and cost aspects when considering deploymentin C-TAR embodiments using exemplary retrofit assembly apparatus may bedeployed on GSE. FIG. 6 is an exemplary table 600 provides exemplaryinformation on exemplary sensor tasks for a combination of exemplarysensors in accordance with an embodiment of the invention.

FIGS. 1B and 2 show different exemplary actuators used as part of anembodiment of a retrofit assembly apparatus for use on tractor 115(e.g., an exemplary logistics ground support equipment) to enhance alevel of autonomous operation of the tractor 115. In general, anactuator may be considered a type of motor that is responsible formoving and or controlling a mechanical system of some type. Actuatorscan be operated by many different means including electric current,hydraulic fluid pressure, or pneumatic pressure which is there processto convert energy of some kind into motion or apply some form of force.The most common types of actuators are cylindrical that perform pistonmovement once energy is applied. Grippers are another type of roboticsactuators but given the functions that may be performed within a cargotractor 115 via C-TAR, piston or piston-like actuators may be deployedin some embodiments. The following provides further information onanticipated types of exemplary actuators that may be used in embodimentsof C-TAR systems with a retrofit assembly apparatus for use on tractor115 to enhance and provide a level of autonomous operation of thetractor 115:

Throttle—Linear or rotary actuators may be used to implement throttleactuator 174 for controlling throttle 154 of engine 142, which may beused in the physical refit of cargo tractors (such as tractor 115) tocontrol fluid flow of the inlet gases into engine 142. The type ofsolution will be dependent on if the cargo tractor is drive-by-wire orbasic hydraulic linkages and how much control at the point of the gaspedal needs to be provided. The drive-by-wire configuration allows forgreater control and more precise movements of the throttle whereashydraulic linkages will cause a greater level of variance. Dieselengines typically implement throttle control by regulating the fuel flowinto the engine. In an embodiment, the refit cargo tractor 115 maybecome a hybrid autonomous vehicle after the refit because humans willstill be capable of controlling the throttle.

Brake—Primary brake actuation may, in an embodiment, be provided by alinear actuator (e.g., a type of brake actuator 172) that attaches tothe frame of tractor 115 and directly moves a manual brake arm (e.g., byeither pushing or pulling the brake pedal toward the floorboard oftractor 115). In an embodiment, potentiometers within the linearactuators could be used to provide feedback and absolute position.Additionally, a separate hydraulic brake actuator could be used toimplement brake actuator 172. As with the throttle and gas pedal, anembodiment may have humans being still be allowed control of and accessto the brake pedal given that an embodiment of such a C-TAR solution mayallow for hybrid autonomy.

Transmission shifter—Depending on the cargo tractor model, thetransmission may be either manual or automatic. Automatic shifteractuators (e.g., a type of transmission actuator 170) may incorporatepositional feedback of any of the gear selector choices (e.g., park,reverse, neutral, drive, etc.). For manual transmissions shifters, anembodiment may use a servomotor as transmission actuator 170 to replacethe existing gearshift mechanism, thereby allowing for a more automaticcontrol. Again, an embodiment may have humans being still be allowedcontrol of and access to the transmission shifter given that anembodiment of such a C-TAR solution may allow for hybrid autonomy.

Steering—Steering can be controlled by a servomotor and controller(e.g., a type of steering actuator 182), which directly drive thesteering column where it attaches to the steering wheel as part ofsteering system 144. This allows the human to still control and drivethe cargo tractor when it is not in a full autonomy control mode.Feedback from the motor to the steering wheel servomotor and controllercould be provided by hall sensors and/or encoders and a control positionsensor could provide absolute position feedback.

Engine crank (crankshaft)—Using internal actuators as engine actuator192 to crank and turn off the engine 142 may be used in an embodiment ashumans may still need external access to the key receptacle for when thecargo tractor 115 is in a hybrid autonomous state.

In general, exemplary embodiments of refit components for such a refitor retrofit assembly apparatus for use on a logistics ground supportequipment to enhance a level of autonomous operation of the logisticsground support equipment may include, but are not limited to, amicroprocessor-based control board that serves as an onboard computer(with memory, interface circuitry, and in some embodiments GUI elements(e.g., graphics interfaces and one or more displays). Such a controlboard may be implemented with a single processor or multiple processors(such as shown in FIG. 3) and software modules that programmaticallyadapt each of the processors to be unconventionally operative to operateas components of the assembly beyond that of a generic computing deviceperforming well-known, routine, or conventional functions as the controlboard (e.g., exemplary control system 140) component of the assembly. Inan exemplary embodiment, the control board may be implemented usingopen-source solutions such as Raspberry Pie and Arduino boards thatprovide cost effective and small format computing based control boardsthat may be programmatically configured to provide unconventional, new,non-routine functionality when deployed in the embodiment. Furtherinformation on exemplary control board implementations appears in Table700 shown in FIG. 7. Embodiments of exemplary control board (e.g.,exemplary control system 140) may be implemented with a light weight,real-time operating system (RTOS). The exemplary RTOS may be based onconstraints such as RAM, storage size, and interrupt handling whichultimately speaks to the speed of the solution and how quickly it can beprogrammed to make quick yet complex decisions based on quickly receiveddata in order to safely and semi-autonomous control cargo tractors andother GSE. Those skilled in the art will appreciate that an exemplaryRTOS for the controller board may include, for example, QNX, variousflavors of Linux, and Xenomai.

Consistent with the description above, the control board (e.g.,exemplary control system 140) generally interfaces and operativelyconnects to one or more sensors (also referred to as a sensor modulewith one or more sensors, sensing elements, or an array of similar ordifferent sensors). These sensors (e.g., exteroceptive sensors) providegenerated sensor data so that the control board, when operating one ormore programs, may collect and analyze the sensor-based data beinggenerated on the GSE vehicle being refit. In an embodiment, the controlboard device will be responsible for controlling braking as well asgoverning the throttle and the speed of the cargo tractor (or otherpiece of GSE) through actuators (e.g., exemplary actuators 225). Thiscontrolled actuation may be based upon the analysis of sensor and otherenvironmental data that may include, for example, GPS data, LightDetection and Ranging (LiDAR) data, Inertial Measurement Unit (IMU)data, Radar data, and camera input data as types of exemplarysensor/environmental data. The sensor module or data logger thatcollects the actual digital and analogue input may physically connect tothe control board via protocols (e.g., USB, GPIB, serial protocols, orother networking/data communication protocols) or using wirelessconnections like Bluetooth, ZigBee, NFC, WiFi, cellular, or the like.

FIG. 3 is a more detailed diagram of an exemplary retrofit assemblyapparatus used on logistics ground support equipment for enhancedautonomous operation shown with logical segmentation of differentelements and roles within the apparatus in accordance with an embodimentof the invention. Referring now to FIG. 3, exemplary system 300implementing an exemplary retrofit assembly apparatus is shown with fourprimary components: exemplary sensors 205 (e.g., exemplary exteroceptivesensors 230 and exemplary proprioceptive sensors 220 as describe aboverelative to FIGS. 1B and 2), an exemplary data preprocessing controlprocessor 305, an exemplary vehicle dynamics control processor 310, andexemplary actuators 225 (e.g., exemplary actuators 225 as describedabove relative to FIGS. 1B and 2). Using these components, exemplarysystem 300 may implement a technical solution with enhanced autonomousretrofit operations on a logistics ground support equipment (e.g.,tractor 115) that provides a refit hardware and sensor package;environment awareness using such a system 300; logic and software forcalculations, data acquisition, and responsive control of the logisticsground support equipment for a level of autonomous operation; andphysics-based determinations of paths for trailing equipment (e.g., oneor more dollies 120).

As shown in FIG. 3, exemplary system 300 deploys exemplary datapreprocessing control processor 305 with various software modules, suchas a data preprocessing module 305 a, a scene understanding module 305b, and a spatial awareness module 305 c. In general, exemplary datapreprocessing module 305 a receives sensor data input from exteroceptivesensors 205 (e.g., camera 188, radar 190, LiDAR 186) where the sensordata may be processed to eliminate discrepant data using the datapreprocessing control processor 305. Exemplary scene understandingmodule 305 b may then receive the processed sensor data from datapreprocessing module 305 a, where the scene understanding module 305 bidentifies objects, classifies objects identified, and estimatesdistances to identified and classified objects to generate at leastrelevant depth information relative to the current state of thelogistics ground support equipment (e.g., the retrofit tractor 115).Exemplary spatial awareness module 305 c generates spatial awarenessinformation relative to the current state of the logistics groundsupport equipment (e.g., the retrofit tractor 115) based upon sensordata input from GPS 158 and/or IMU 156.

As shown in FIG. 3, exemplary system 300 deploys exemplary vehicledynamics control processor 310 with various software modules, such as avehicle dynamics model 310 a, databases 310 b (with map, object, andvehicle operating parameter information), and a model predictive control(MPC) module 310 c. In operation, the exemplary vehicle dynamics controlprocessor 310 executes these software modules to access information fromvehicle dynamics model 310 a and databases 310 b, receive the generateddepth and spatial awareness information from exemplary datapreprocessing control processor 305, and receive sensor data fromproprioceptive sensors 220, determine an optimized path for retrofittractor 115, and activate one or more of actuators 225 according to theoptimized path determined that autonomously alters motion of the refittractor 115.

In more detail, an embodiment of exemplary model predictive control(MPC) module 310 c may determine control solutions to determine GSEmovement (e.g., the maximum allowable speed) at a discrete moment intime/space related to an optimized path. More particularly, anembodiment of the MPC 310 c may employ a look-ahead policy (e.g.,exemplary look-ahead steps 1315, 1320, and 1325 illustrated in FIG. 13relative to a current state 1305 of the GSE) and is applicable todiscrete event management using supervisory control. In FIG. 13, anexemplary search tree diagram 1300 for predictive control is shown aninitial state 1305 is shown as an exemplary past state of the GSE andwhere the GSE has moved to current state 1310. An embodiment ofexemplary MPC 310 c may predict different reachable states underdifferent operative control conditions (e.g., speeds, direction, etc.)so as to then consider and evaluate such states and whether they may beconsidered invalid reachable states (e.g., within the envelope 1330where, for example, a predicted location of an environmental object liesor where an operational rule (such as a turning radius limit or speedlimit based on location) is inconsistent with the state). In otherwords, an embodiment of exemplary MPC 310 c may calculate possiblecontrol outcomes for the set of control inputs over a limited predictionhorizon. With a performance evaluation function (also referred to as a“cost” function related to a performance metric), an embodiment of theMPC 310 c may predict and evaluate reachable system states in theaforementioned prediction horizon (e.g., as illustrated with states 1325shown in FIG. 13) such that an optimal outcome can be found (e.g., apath may be determined without being an invalid reachable state 1330),and the corresponding system control input can be selected and providedvia vehicle motion control 310 d to one or more of actuators 225. Forexample, in one embodiment, the use of “optimal” may mean the predictedcontrol solutions along the most realizable path for collision avoidancewhich results in the least limiting of vehicle speed while ensuringcollision are prevented. This process may repeat until a predefinedobjective is reached, such as system 300 operating in a safe zone awayfrom passive beacons 105 a-105 d and other obstacles (e.g., detectedenvironmental objects, such as other vehicles, humans, and the like).The vehicle dynamics control processor 310 part of the exemplary system300 includes a vehicle motion control software module 310 d thatimplements a vehicle actuation feedback control system using the vehicledynamics information 310 a and operating as a feedback compensator toprovide input to vehicle actuators 225 (such as actuators for the cargotractor's throttle 154 and/or braking system 148 and/or a gear selectoron transmission system 146 on the cargo tractor 115).

FIG. 4 is a diagram of exemplary implementation details for parts of anexemplary retrofit assembly apparatus used on logistics ground supportequipment for enhanced autonomous operation in accordance with anembodiment of the invention. As shown in FIG. 4, similar processingfunctionality as described above with respect to FIG. 3 is shown in alogical arrangement with an exemplary sensor package 405 (e.g., sensorsthat include exteroceptive (including an ultrasound sensor 420 and a lowpower Bluetooth sensor 425 in addition to others described above) andproprioceptive sensors). Exemplary vehicle motion control module 310 dis shown receiving inputs from various proprioceptive sensors as well asinput from the MPC 310 c. As shown in FIG. 4, exemplary MPC 310 c (aspart of the exemplary software modules 415 operating as part of system400 on refit control system 140) takes sensor data input fromexteroceptive sensors in sensor package 405. Modules 415 process thesensor data using signal processing modules 305 a for preprocessingsensor data from ultrasound sensor 420, LiDAR 186, and camera 188.Exemplary scene understanding module 305 b then uses the preprocessedsensor data with object detection code processing sensor data fromultrasound sensor 420 as preprocessed by module 305 a, with objectdetection code and beacon detection code processing sensor data fromLiDAR sensor 186, and with object recognition code processing image datafrom camera 188. Spatial awareness module 305 c generates spatialawareness information relative to the current state of the logisticsground support equipment (e.g., the retrofit tractor 115) based uponsensor data input from low power Bluetooth sensor 425, GPS 158 and IMU156 in sensor package 405. Exemplary data fusion modules 405, 410 maytake object detection information, beacon detection information, andobject recognition information from module 305 b along with informationfrom databases 310 b to provide inputs to MPC 310 c. MPC 310 c thenprovides its output to vehicle motion control module 310 d, which maygenerate relevant actuator control signals in order to activate one ormore of actuators 225 according to the optimized path informationgenerated by MPC 310 c.

In some situations, data captured from the real world may be considereddirty (noisy) and incomplete as it may lack attribute values, certainattributes of interest, and only contain aggregate data or errors andoutliers. By having exemplary C-TAR retrofit assembly apparatus on atractor 115 and the larger robotic AI functionality of refit controlsystem 140 perform data preprocessing (e.g., as performed using datapreprocessing module 305 a running on data preprocessing controlprocessor 305), inconsistent and discrepant data could be eliminatedthereby only leaving what's needed for movement and other autonomyrelated functions. In an embodiment, knowledge discovery on the part ofthe robotic AI may be a potential output as data preprocessing includescleaning, normalization, transformation, feature extraction, selection,etc.

In some embodiments, LiDAR 186 and other similar exteroceptive sensors230 may also be used to provide data for depth and spatial awareness foran exemplary C-TAR system and the AI component of the refit cargotractor (e.g., have such sensor data fed to spatial awareness module 305c for determining depth and spatial awareness information). From aspatial awareness perspective, some embodiments may update frequently sothat awareness stays sufficiently up to date (given movementconsiderations) and accurate even during low visibility conditions. GPS158 may be used to help pinpoint the location and route while one ormore cameras 188 look at identified patterns (e.g., repeating patterns)like lane markings and speed limits. Some cameras 188 may, in someembodiments, be used to identify other vehicles, aircraft, road signs,pedestrians, signals, containers, facility walls and components, andanything else that could be found at an airport or logistics facility.Spatial awareness information generated by module 305 c may includecreating data that could be used by a single autonomous vehicle (e.g.,refit tractor 115) or a collection of vehicles by way of a computational“farm”, which could make calculations and decisions more quickly thanthe refit cargo tractor 115 itself. The amount of data captured for truespatial awareness may, in some embodiments, be so large that multiplecomputers (e.g., multiple processors dedicated for running spatialawareness module 305 c and/or scene understanding module 305 b) may beused to identify pertinent information and generate depth and spatialawareness information accordingly.

Relative to path optimization, for AI logic used on a control board(e.g., a controller using one or more processors, such as exemplarycontrol system 140) in an exemplary C-TAR retrofit assembly apparatusused on refit tractor 115 to enhance a level of autonomous operation ofthe tractor 115, efficient real-time autonomous driving motion planningand coordination as well as trajectory optimization may be used andimplemented as part of MPC 310 c. Based on AI-based cost functions (suchas the environments of the airport or other industrial facility, theprocess being performed, the timeframe requirement, and the safetyconcerns), the planner defined using path optimization could discretizethe plan space and search for the best trajectory for the C-TAR enabledGSE (e.g., refit tractor 115). Then, an iterative optimization may beapplied to both the path being driven by the cargo tractor 115 and itsspeed of the resultant trajectory. In an embodiment, the simplest costfunction in path optimization may be considered distance becausemodifications can be calculated in a two-dimensional plane. For morecomplex cost functions that involve the manipulation of time and energyin further embodiments, the analysis may be performed in threedimensions. As such, for an exemplary embodiment, a one-dimensionaloptimization of the motions along a specified transit path and a searchfor optimal path in the position space could be two separate componentsthat the AI of the autonomous vehicle (e.g., the code in exemplary MPC310 c) calculate in order to reduce complexity.

Embodiments of path optimization on a refit GSE (e.g., a C-TAR retrofitlogistics GSE such as refit tractor 115) may, for example, include anawareness of and calculation for multiple components such as timeoptimization motion along pre-specified paths. This component may focuson maximum acceleration and deceleration at every point. Localoptimization is the component of path optimization that finds the pathwith the minimum time. Sometimes this can be based on complete knowledgeof the surrounding layout of the environment (e.g., contextual data usedby the onboard controller) or based on a “guess” where past history(e.g., another type of contextual data based on historical information)or unfamiliar conditions may dictate a more preferred movement based onether human or robotic conservation (e.g., prompted contextual data).Global optimization may consider the entire environment (the entirespace in which a refit cargo tractor can traverse) through awarenesscreated by possible technologies such as geo-fencing. Globaloptimization may also take into account a fleet or swarm of semi- orfully autonomous vehicles in an environment where a master node capturesthe environmental data and does most of the processing for theindividual slave nodes which are in fact the refit cargo tractorsthemselves. In other words, the master node may be a central computingtype of device that communicates, respectively, to different refit cargotractors.

In an exemplary C-TAR embodiment, the global environment may includepaths through covered areas inside buildings. Path optimization may alsoneed to have an awareness of transitioning into and out of buildingswhere GPS, LiDAR, and other technologies will be limited. The refittractor 115 may need to understand two distinctly different states:being in an outside uncovered environment and being inside a coveredenvironment. An embodiment may use different safety and process rules toapply under the two different conditions thereby further modifying howpath optimization may be accomplished based on such different geographicinformation (e.g., map information from database 310 b).

In an exemplary embodiment, a vehicle dynamics controller (e.g., vehicledynamics control processor 310 as part of refit control system 140) maybe concerned with items like roll, pitch, and yaw as well as vertical,lateral, and longitudinal dynamics via multiple inputs. The areas of avehicle influenced by vehicle dynamic control may include the frontsteering 144, brakes 148, engine torque of engine 142, suspensions ofthe tractor, and active differential for transmission system 146.Because embodiments of the refit cargo tractor 115 may be semi- or fullyautonomously controlled, these dynamics systems may be integrated withlocal and global controller measurements coming from such systems as GPS158, cameras 188, infrared (another type of exteroceptive sensor), radar190, LiDAR 186, etc. Thus, an embodiment may deploy an integratedvehicle dynamics controller to take over calculations of position andvelocity in a global frame. This may be implemented as part of vehiclemotion control module 310 d running on vehicle dynamics controlprocessor 310 or as a separate module running on vehicle dynamicscontrol processor 310 or data preprocessing control processor 305.

Various embodiments may be used for a semi- or fully-autonomous roboticvehicular system for an exemplary C-TAR embodiment. For example, thismay include single robot, multi-robot, or hybrid systems.

Single robot systems—An exemplary refit cargo tractor, semi- or fullyautonomous vehicle, or robotic AI driven device may operate independentof all other autonomous entities. Data captured by one C-TAR refitdevice would keep that information for processing and learninginternally only. While each unit will react accordingly to other units,this reaction will be based on their internal programming and notinformation being processes by other refit C-TAR cargo tractors. Eachunit may build up and store a data repository on the environment, but itwill be information that can only be utilized by the unit that createdit.

Multi-robot systems—Unlike the single robot systems, an embodimentdeploying a multi-robot system may be used to share data amongst allsemi- or fully autonomous refit vehicles in the environment. As onerefit vehicle learns information about an aspect of their environment,all units may gain that information via a central hub (e.g., masternode, central server, or other centralized communication and sharingdevice) for data and communication. In such an exemplary multi-robotsystem embodiment, the majority of complex computing may occur in acentral location with the results of data processing being transmittedto the individual cargo tractors for instruction and process control.This creates a “hive” AI mentality where a central unit, the masternode, is in control of all of the servant nodes. Having complexcalculations and data processing occur in a central location lessens theneed for more robust computing systems within each C-TAR enabled refitvehicle.

Hybrid systems—Both single and multi-robot implemented systems havebenefits and limitations. For instance, a single robot system of cargotractors could keep equal awareness of all environment whether theserefit systems transition into or out of a building or are outside in anopen space. However, a refit cargo tractor may remain largelydisconnected from the fleet and, as a result, may have to learn the sameinformation even if other cargo tractors in the same environment havealready processed complex response and reactionary information.Likewise, multi-robot systems often use a central point of processingand site to observe and control all of the servant nodes. Transitioninginto and out of buildings may be very difficult for a multi-robotsystems comprised of cargo tractors at a large airport hub that handlesa large amount of logistics. Passing into a building in such anenvironment may cause disruptions as a higher emphasis on technologieslike GPS will be required. As such, an embodiment may combine both ofthese methodologies for vehicle fleet autonomy because each environmentof the complex airport hub—outside on the tarmac and inside a sortationfacility—may have different requirements, needs, and data forprocessing.

The state of a complex airport hub operating environment (similar tothat shown in FIG. 1A) may be such that many or all GSE are human drivenand completely human controlled (with some exception to aircraft 100).One embodiment of C-TAR may be implemented so that human driven cargotractors operate to complement human decision making and increase safetyby preventing actions that would otherwise cause harm or damage. Forexample, an embodiment of a C-TAR retrofit assembly apparatus for use onGSE may increase safety so that refit cargo tractors never have animpact with humans, aircraft, other vehicles, or anything specified bythe robotic AI.

Another advantage of an embodiment may be efficiency. For example, incertain embodiments, the refit cargo tractor may take control from thehuman in order to marry up the vehicle and dolly string with the loaderof an aircraft to prevent plane strikes or poor alignment. When thevehicle does take control from the human, the C-TAR unit (as part of therefit GSE) could provide some type of feedback indication (e.g., visual,audible, or haptic) letting the driver and/or passengers know what isabout to happen, why, and when the autonomous control period will end.Communication through any means with humans in the vehicle could be animportant component while the cargo tractors are running in asemi-autonomous mode.

Further embodiments may deactivate the autonomous systems of a C-TARretrofit assembly apparatus for use on GSE based onhuman/operator/driver detection in the seat or detection ofingress/egress of the human/operator/driver relative to the GSE using anoperator detection sensor 160. The refit cargo tractor 115 may be awareof human passengers (e.g., operators, passengers, drivers, and the like)and react accordingly. In some embodiments, full autonomy may not beallowed when a human is present. Only key safety and efficiency systemsshould be enabled when a person is in the seat. Likewise, no humanshould be allowed on a refit cargo tractor while it's in full autonomousmode.

For example, such an operator detection sensor 160 may be deployed ontractor 115 as one of the refit/retrofitted and added proprioceptivesensors. As such, the refit control system 140 may be programmaticallyconfigured to activate the at least one of the actuators 225 from alimited subset of the actuators 225 based upon detection data generatedby the operator detection sensor 160. But in another example, the refitcontrol system 140 may be programmatically configured to preventactivation of one or more (or all) actuators 225 based upon detectiondata generated by the operator detection sensor 160.

In an embodiment, when a human is driving refit tractor 115, anexemplary C-TAR retrofit assembly apparatus for use on GSE may take overcontrol of the cargo tractor 115. In such an embodiment, an exemplaryform of feedback may be provided to the human to let them know somethingis about to happen. For example, an embodiment may have an exemplaryC-TAR retrofit assembly apparatus for use on GSE include an exemplaryfeedback user interface coupled to the refit control system 140 (asnoted above), where the feedback user interface is responsive tonotification input generated by the refit control system 140 andoperative to generate operator feedback information as feedback for anoperator of the logistics ground support equipment. Such notificationinput generated by the refit control system 140 is triggered when therefit control system 140 activates the at least one of the actuators 225according to the optimized path for the logistics ground supportequipment. The operator feedback information may, for example, be anindication that the logistics ground support equipment is autonomouslyslowing down, be an indication that a steering wheel of steering system144 has been autonomously locked from manual control, and/or be a visualor audible indication that a state of the logistics ground supportequipment has autonomously changed via a display on the dashboard oftractor 115 and/or speaker on tractor 115 and/or status lights disposedon tractor 115. Similar feedback may be provided external to theoperator's view of tractor 115 (e.g., a display and/or status lightsexternally focused) so that when the tractor 115 is being drivenautonomously, people walking in the same environment or manually drivingvehicles within the environment always know the intent of the robotic AIimplemented as part of the exemplary retrofit assembly apparatus ontractor 115. As such, visual feedback on the outside of the tractor 115may be possible, as well as audible feedback such as an automated hornbeep may be activated at every stop sign and before entering or leavinga building.

Embodiments that retrofit or refit C-TAR technology described above intotypes of GSE may provide a variety of technological advantages andsolutions to existing technical problems. For example, such benefits andsolutions that are new and unconventional may include modifying existingcargo tractors to assist humans with safety and collision avoidance inan airport or regulated logistics environment; refitting cargo tractorsso that they autonomously detect a human presence, respond with actuatedchanges to the path of the tractor, and communicate autonomous intentback to human drivers and passengers; modifying cargo tractors so thatthey take control over from the human to align movement in conjunctionwith other AI “recognized” vehicles types to ensure damage reduction andincreased efficiency; and partial or full robotic AI kinetic control ofa vehicle not originally intended to be semi- or fully autonomous.

Embodiments of this C-TAR technology solution could be used on GSE(e.g., cargo tractors) or any other similar vehicle type or vehicletypes in similar industrial environments. Additional vehicle examplesinclude fork lifts, golf carts, aircraft loaders, “people movers”, yardmules, etc. Any vehicle type that's in a well-defined, well planned, andhighly regulated environment and, as defined by industrial processes,will not be expected to leave said environment could be refit given thatmost autonomous rules are known and could be programmed into the AI. Anyenvironment where it is not economically feasible to replace an existingfleet but the fleet could be refit with C-TAR embodiments usingexemplary retrofit assembly apparatus that assumes at least some levelof control to ensure the driver isn't hurt, doesn't hurt others, doesn'tcause pre-defined damage, and limits use to only process allowedconstraints (such as only being driven in certain parts of a facility).Embodiments of such a solution could be used anywhere that a vehicleneeds to provide feedback to the driver that's specific to their workprocesses and the known intent of their actions (such as driving to acertain part of the tarmac on which an aircraft operates to retrieve aULD container where the vehicle informs the driver that they are drivingin the wrong direction, not using the shortest route, or not using anapproved route). Such embodiments may be used with vehicles that needthe ability to use predictive stopping and acceleration based on rulesand regulations and influenced by changing external variables suchweather, time of day, time of year, traffic congestion, etc. With muchmore sensitive controls in place, the current limitations of the vehicledue to having human drivers can be modified such as allowed speed, towweight, number of trailing dollies, turn radius, etc.

Further embodiments may include modifications to the object detectionand recognition aspects described above (e.g., as part of exemplaryscene understanding module 305 b) to facilitate the detection andrecognition of temporary landmarks such as people, cones, and otherpieces of equipment like loaders, dollies, and GSE to permanentlandmarks like light and power poles, buildings, signage, barriers tovirtual landmarks like road boundaries provided via geofencing.Furthermore, an embodiment may have an aircraft be refit and modified inorder to communicate with one or more parts of the C-TAR embodimentsusing exemplary retrofit assembly apparatus (e.g., a wirelesstransceiver deployed as part of the refit control system 140 that allowsfor communications with external devise, such as servers, other refitGSE, and transceivers onboard aircraft 100).

In an embodiment, detection, recognition, and/or communication with suchelements may occur in a variety of ways. For example, exemplaryenvironment communication solutions may involve using marking or visiblestrips or other identifiable objects/symbols (such as magnetic tapestriping) to identify key objects or situations in which one or moreparts of the C-TAR embodiment should respond (or not). In more detail,an embodiment similar to that shown in FIG. 1A may place magneticallystriped cones (e.g., reflective beacons 105 a-105 d) in key placesaround the aircraft at which the retrofit assembly installed on therefit cargo tractor 115 may detect, based on sensor input used to “see”the striping to either slow or fully stop based on proximity to themodified cones. Likewise, another embodiment may have parts of aircraft100 and loaders 110 be striped or otherwise deployed with markings oridentifiable symbols so that one or more parts of the C-TAR embodimentmay detect, sense, and become aware of how the refit GSE should line upwith the aircraft 100 for loading/unloading purposes while avoidingstriking the aircraft 100. Striping, or some other marking element maybe applied in different formations so that the retrofit cargo tractor115 (or like piece of equipment) may use the retrofit assembly apparatusto know to slow and stop when around aircraft 100 but only slow and notstop around cargo dollies 120 so that the cargo tractor 115 can still beused to push the dolly 120 around for positioning purposes.

In further embodiments, additional active technical environmentsolutions may include electronics that use standard communicativeprotocols such as Bluetooth, RFID, IEEE 802.11, ZigBee, Cellular, NFC,etc. and transmit information about key components of the environment toone or more parts of the retrofit assembly apparatus onboard the refitGSE platform (e.g., the refit control system 140). In general, such anembodiment essentially provides a layer of contextual awareness toinanimate objects positioned around the facility and allows theenvironment to tell—in real time—the refit C-TAR-enabled equipment whatto do (or not do), what to know, how to interact, and so on. Thistechnical equipment that could be installed throughout a facility mayessentially utilize the same concepts as the IoT (Internet of Things)where there is a computational component, a series of various sensorsthat identify information about the environment, and a communicativebackbone (e.g., a transmitter, transceiver, and the like) that may beimplemented in hardware alone or in a combination of hardware andsoftware (e.g., software-defined radios as a type of communicativebackbone).

Furthermore, an embodiment may create zones within an environment (suchas that shown in FIG. 1A and FIG. 8) that designate how fast a refitcargo tractor 115 or other GSE should go when a collision avoidanceobject is detected, such as beacon 105 a shown in FIG. 8. FIG. 8 isdiagram illustrating the use of zones around an exemplary beacon device105 a that may be used as a reference point type of environmental objectbeing detected by an exemplary retrofit assembly apparatus used onlogistics ground support equipment for enhanced autonomous operation inaccordance with an embodiment of the invention. Referring now to FIG. 8,beacon device 105 a (e.g., an IoT type of broadcasting device) may beused as a reference point for a refit GSE, such as tractor 15, using anembodiment of a C-TAR retrofit assembly apparatus described above toestablish different zones (e.g., warning zone 800, speed limit zone 805,reduced speed limit zone 810, and critical zone for no entry 815) thatmay designate speed limits and provide further warning information tothe refit GSE for use by the onboard systems when controlling andgoverning the speed of such a refit GSE. Such zoning information may beprovided and available to the refit GSE as location or geo-fencing typeof information with which to use as reference data (e.g., map and/orobject information maintained in database 310 b shown in FIGS. 3 and 4).This same zoning concept could be applied to further items/objects inthe environment and controlled by virtual geofencing (such as, forexample, removing any governed restrictions while the refit cargotractor 115 is on a designated straight away or road according to mapinformation in database 310 b or by using virtual zones based on theplacement of an object on a refit vehicle's proximity to the object(such as, for example, gradually slowing the cargo tractor as it entersvarious zones and gets closer to cones positioned around aircraft 100).

Embodiments may have exemplary MPC 310 c identifying the relativelocation, placement, and movement of multiple trailing dollies 120relative to refit tractor 115 and make movement decisions for the GSE aspart of optimizing a path for the refit tractor 115. In general, suchembodiments may have the refit control system 140 on the refit GSEprogrammed via MPC 310 c so as to be unconventionally operative tocalculate a polygon that identifies the placement of multiple trailingdollies and make movement decisions based on the knowledge of trailingcargo dollies (loaded or unloaded) in respect to the refit GSE'sapproach to an aircraft or any other piece of equipment and facility.This polygon will take into account turning radius of trailing dollies(as illustrated in FIG. 13) while the braking system keeps an awarenessof the total amount of weight being pulled in order to accurately adjustfor breaking and stopping.

FIGS. 9-13 include further information on embodiments of the controlsystem for a retrofit assembly apparatus used on a refit GSE that mayimplement exemplary MPC 310 c with calculation logic for such a solutionthat allows a refit GSE (such as tractor 115) to make autonomousdecisions on movement with trailing dollies. FIG. 9 is an exemplaryframework diagram of an exemplary dynamic modeling framework thatdetermines the instantaneous status of an exemplary cargo tractor-dollysystem in accordance with an embodiment of the invention. As shown inFIG. 9, an exemplary dynamic modeling framework 900-915 determines theinstantaneous status of towing vehicle systems by employing a statespace model 910 and cargo tractor-dolly polygon model 915 to calculateinstantaneous positions and velocities for the towing vehicle based, forexample, on Newton's second law to estimate the positions of thesubsequent towed vehicles by assuming that each towed unit follows thesame path (using a sequence of directional angles provided by statespace model 910 to model 915) as the towing vehicle. The state spacemodel 910 calculates the instantaneous positions and velocities of thetowing vehicle based on its initial conditions collected from IMU 156and/or GPS 158. The position calculated using the state space model 910represents the position of a reference point at the towing vehicle andthe real-time shape of the towing vehicle will be determined based onthe coordinate of that point and its dimensions. For example, as shownin FIG. 10, a single cargo tractor 115′ is illustrated as a shape 1000with vertices, an exemplary reference point 1010 located on the tractor115′, and a boundary or exemplary collision envelope 1005 encompassingtractor 115′ so as to provide a buffer zone from the envelope 1005relative to the shape of tractor 115′. The relative position of each ofthe vertices 1015 a-1015 d of the shape of tractor 115 with respect tothe local reference point 1010 may be transformed to a global referencepoint in the operating environment of the tractor 115 by the cargotractor-dolly polygon model 915. A similar approach may then applied todetermine the instantaneous shapes of the subsequent vehicles, asreflected in shapes 1100 shown in FIG. 11 with exemplary reference point1110 located on the tractor 115′, another local reference point 1115 ondolly 120′ (which is disposed at an angle relative to tractor 115′), anda boundary or exemplary collision envelope 1105 encompassing tractor115′ and dolly 120′ so as to provide a buffer zone from the envelope1105 relative to the combined shape 1100 of tractor 115′ and a singledolly 120′. This may be extended for a train 1200 of cargo dollies 120a-120 d shown in FIG. 12 being pulled by tractor 115 a (with exemplarytow bars 1205 a-1205 d connecting the dollies to the unit in front ofthe respective dolly).

In light of the description above, an embodiment of an exemplaryretrofit assembly apparatus for use on logistics ground supportequipment (e.g., cargo tractor 115) may enhance a level of autonomousoperation of the logistics ground support equipment. In this embodiment,the assembly includes a refit control system attached to the logisticsground support equipment, retrofitted sensors coupled to the refitcontrol system, and actuators coupled to the refit control system. Inparticular, the refit control system (e.g., control system 140) mayinclude a vehicle dynamics control processor (e.g., processor 310 asexplained relative to FIGS. 3 and 4) and a data preprocessing controlprocessor (e.g., processor 305 as explained relative to FIGS. 3 and 4).The retrofitted sensors coupled to the refit control system are disposedon the logistics ground support equipment as retrofitted equipment tothe logistics ground support equipment. Such retrofitted sensors in thisembodiment include a group of proprioceptive sensors (e.g., one or moreof sensors 220) coupled to the vehicle dynamics control processor thatmonitor operating parameters and characteristics of the logistics groundsupport equipment, and a group of exteroceptive sensors (e.g., one ormore of sensors 230) coupled to the data preprocessing control processorthat monitor an exterior environment of the logistics ground supportequipment. Each of the actuators (e.g., one or more of actuators 225)are disposed on the logistics ground support equipment as a retrofitactuator to add control of different control elements (e.g., steering144, braking system 148, transmission 146, engine 142, throttle 154) onthe logistics ground support equipment and to autonomously alter motionof the logistics ground support equipment. The refit control system(e.g., control system 140 that may be implemented with vehicle dynamicscontrol processor 310, data preprocessing control processor 305, andtheir respective software modules described relative to FIGS. 3 and 4)is programmatically configured to be operative to receive first sensordata generated by the group of proprioceptive sensors using the vehicledynamics control processor of the refit control system, and receivesecond sensor data generated by the group of exteroceptive sensors usingthe data preprocessing control processor. The programmaticallyconfigured refit control system is further operative to optimize a pathfor the logistics ground support equipment based upon the second sensordata from the data preprocessing control processor and the first sensordata from the vehicle dynamics control processor, and then activate(using the vehicle dynamics control processor) at least one of theactuators according to the optimized path for the logistics groundsupport equipment.

In a further embodiment, the programmatically configured refit controlsystem may be further operative to eliminate discrepant data from thesecond sensor data using the data preprocessing control processor andgenerate depth and spatial awareness information relative to the currentstate of the logistics ground support equipment using the datapreprocessing control processor. As such, the programmaticallyconfigured refit control system may then be operative to optimize thepath for the logistics ground support equipment based upon the depth andspatial awareness information from the data preprocessing controlprocessor and the first sensor data from the vehicle dynamics controlprocessor.

In another embodiment, the vehicle dynamics control processor may beoperative to activate the at least one of the actuators according to theoptimized path for the logistics ground support equipment to cause oneor more of the control elements on the logistics ground supportequipment to alter the motion of the logistics ground support equipment(e.g., to change settings on one or more of the control elementsresulting in a change in the motion of the logistics ground supportequipment or to meet a vector requirement defined by a determinedheading and/or altered speed associated with the optimized path).

In still another embodiment, the data preprocessing control processormay be programmatically configured to eliminate the discrepant data fromthe second sensor data by being further operative to transform thesecond sensor data to extract a set of predetermined attributes relatedto autonomous movement from the discrepant data. The data preprocessingcontrol processor may also be programmatically configured to generatethe depth and spatial awareness information relative to the currentstate of the logistics ground support equipment by being furtheroperative to (a) receive the second sensor data over a period of time;identify a pattern from the received second sensor data (where theidentified pattern is associated with at least one environmental objectexternal to the logistics ground support equipment, and where theidentified pattern associated with the environmental object is at leastpart of the depth and spatial awareness information); (b) identify atleast a second environmental object related to a current location of thelogistics ground support equipment as determined from location data inthe received second sensor data (where the identified secondenvironmental object is also at least part of the depth and spatialawareness information); and (c) identify a third environmental objectfrom one or more exteroceptive sensor data types in the received secondsensor data (where the identified third environmental object is at leastpart of the depth and spatial awareness information). More particularly,in this embodiment, the exteroceptive sensor data types in the secondsensor data may be visual image data representing a predetermined areaproximate the logistics ground support equipment, radar datarepresenting a predetermined area proximate the logistics ground supportequipment, multi-dimensional map data representing a predetermined areaproximate the logistics ground support equipment, and/or proximity datarepresenting a distance between the logistics ground support equipmentand one or more of the environmental objects. Further, the environmentalobjects may, for example, be a lane marking, a road sign, a vehicledisposed external to the logistics ground support equipment, anaircraft, a pedestrian, a signal light, a container, and/or a facilitywall.

The above-described embodiments that encompass technology solutions maybe used on logistics ground support equipment, such as cargo tugs ortractors or any other similar vehicle type or vehicle types in similarindustrial environments. Additional logistics ground support equipmentexamples include but are not limited to fork lifts, golf carts, aircraftloaders, “people movers”, yard mules, and the like. Any vehicle typethat's in a defined and regulated environment, as defined by industrialprocesses, and that will not be expected to leave such an environmentmay be refit with one or more of the above-described embodiments thatmay improve their automated, autonomous, or other operations in orderto, for example, avoid collisions or create governed rules about theenvironment and objects within that environment. Any environment whereit is not economically feasible to replace an existing fleet, may be asuitable candidate for one or more aspects of the above-describedtechnology where the fleet could be refit with technology like thatdescribed herein assuming some level of control to ensure the driver isnot hurt, does not hurt others, does not cause pre-defined damage toother equipment such as aircraft, and limits use to only process allowedconstraints (such as only being driven in certain parts of a facility).For example, one or more aspects of the above-described technologysolutions may be used anywhere that a logistics support vehicle providesfeedback to the driver that is specific to regarding speed zones and whycertain areas are speed governed. This type of solution may also be usedwith vehicles that need the ability to use predictive stopping andacceleration based on rules, regulations, zones, and contextualawareness.

As described above and in the related Figures, those skilled in the artwill appreciate that there may be a variety of advantages or benefits ofdifferent embodiments over currently available technology. For example,one or more embodiments as described herein may provide technicalsolutions that address particular problems and include one or more ofthe following advantages or benefits:

1. Increased safety for all people in the environment

2. Reduced aircraft strikes and other costly errors caused by humansdriving large and heavy industrial equipment that may prevent a finalproduct from being delivered or cost organizations unnecessary expense

3. Increased efficiency of processes by preventing actions that couldcause disruptions and ultimately waste time by making some decisions onbehalf of the human driver

4. Decreased expense in marginal costs like fuel usage and employee time

5. Decreased dependency on humans to get necessary job functionscompleted there by mitigating the employee turnover problem currentlybeing experienced in places like the Memphis Hub; there won't be theneed to decrease employed workers but there will be an opportunity todecrease the number of job openings currently available in largerfacilities

6. Increased throughput like potentially more dollies being pulled by asingle cargo tractor; for example, current limitation is four dolliesper tug but if the vehicle, through robotic AI and a designation ofzones, is intelligent enough to control speed and braking in a way thatretains existing safety parameters then a fifth dolly could be addedthereby reducing trips to aircraft

7. Decreased cost in the purchase and implementation of a semi- or fullyautonomous cargo tractor fleet

8. Increased understanding of where autonomy can realistically beimplemented in a pre-existing airport or logistics facility using aspecific and key component of the loading and off-loading part of theaircraft container movement process

Adaptive Control Using Feedback Loop Monitoring

Further embodiments may focus on an exemplary adaptive control systemusing feedback loop monitoring for improved engine performance and amethod for using the same on logistics ground support equipment enhancedwith such an adaptive control system. As noted above generally, theremay a measurable delay between when a throttle on a GSE allows more airinto the gasoline engine and when the engine power increases allowingthe vehicle to move. An embodiment that deploys an exemplary adaptivecontrol system using feedback loop monitoring aims to command a certainengine power level (e.g., a type of engine operating parameter) and maygauge one or more control feedback loop iterations in order to achievethe preferred engine performance. Those skilled in the art willappreciate that the control system feedback loop monitoring within C-TARmay vary in sensitivity across the multiple brands and their models ofGSE. In general, such embodiments may help by proactively addressinglatency and response time for logistics ground support equipment, suchas cargo tug 115, with considerations of sensitivity to load (e.g.,weight of cargo being transported by the GSE), temperature, position(e.g., on a ramp area where cargo loading/unloading may involverestricted operations), detected obstacles, location & orientation ofthe GSE (e.g., if the GSE is near a plane, lowering speed), buildingtransitions, as well as weight/grade to improve operations of theequipment.

An embodiment of such an exemplary adaptive control system for improvedengine performance may be deployed on logistics ground supportequipment, such as tractor 115 as shown in FIG. 1B and with control,sensor, and actuator systems explained further in FIGS. 2-4. In moredetail, such an embodiment of an exemplary adaptive control system mayinclude a control system (e.g., refit control system 140 attached totractor 115), sensors coupled to the control system (e.g., sensors 205),and at least a throttle actuator coupled to the throttle of the GSE'sengine. For example, in this embodiment, refit control system 140 may beused the control system part of such an exemplary adaptive controlsystem and may be implemented with vehicle dynamics control processor310 and data preprocessing control processor 305. The sensors coupled tothe control system are disposed on the tractor 115, and include aproprioceptive sensor (e.g., exemplary engine RPM sensor 180,transmission status sensor 166, or an airflow sensor (not shown))coupled to the vehicle dynamics control processor 301 that monitors anengine operating parameter (e.g., engine power generated) of the engine142 of tractor 115. In this embodiment, the sensors coupled to thecontrol system also include a second group of exteroceptive sensors(e.g., sensors 230) coupled to the data preprocessing control processor305 (as shown in FIG. 3) that monitor an exterior environment of thetractor 115. In this example embodiment, the throttle actuator may beimplemented with exemplary throttle actuator 174 coupled to the vehicledynamics control processor 310 of the control system (e.g., via vehiclemotion control software module 310 d running on processor 310) anddisposed on the tractor 115 to be in responsive communication with thethrottle control 154 for engine 142.

In this embodiment, refit control system 140 as described above isprogrammatically configured to be operative as part of the exemplaryadaptive control system for improved engine performance to (a) receivesensor data (first sensor data) generated by the proprioceptive sensorusing the vehicle dynamics control processor 310 where this first sensordata indicators a current state of the engine operating parameter; (b)receive additional sensor data (second sensor data) generated by thegroup of exteroceptive sensors using the data preprocessing controlprocessor 305, where this second sensor data indicates a current stateof the exterior environment of the tractor 115; (c) determine, by thevehicle dynamics control processor 310, a predicted state of theexterior environment based upon the second sensor data; (d) proactivelyadjust the throttle control 154 for the engine with an adjustment signalsupplied by the vehicle dynamics control processor 310 to the throttleactuator 174 (where the level of the adjustment signal is based upon thefirst sensor data and the second sensor data); and (e) repeat functions(a)-(d) in an adaptive feedback loop to address latency in performanceof the engine 142 of the refit tractor 115.

In more detail embodiments, the predicted state of the exteriorenvironment of tractor 115 may be implemented using particularexteroceptive sensor-based information as generated by the dataprocessing control processor 305 and provided to the vehicle dynamicscontrol processor 310 (e.g., exemplary MPC 310 c as executing on vehicledynamics control processor 310). For example, the predicted state of theexterior environment of tractor 115 may be determined by the vehicledynamics control processor 310 based upon spatial awareness informationprovided to the vehicle dynamics control processor 310 by spatialawareness module 305 c running on data preprocessing control processor305. Such spatial awareness information may be generated by the dataprocessing control processor 305 via spatial awareness module 305 cusing the second sensor data received by the data preprocessing controlprocessor 305 (e.g., location data from GPS 158 and/or IMU 156representing a current position of the tractor 115 as at least part ofthe sensor data received by the data preprocessing control processor305. In even more detail, the predicted state of the exteriorenvironment of tractor 115 may be determined by the vehicle dynamicscontrol processor 310 based upon (i) spatial awareness informationprovided to MPC 310 c running on vehicle dynamics control processor 310by spatial awareness module 305 c running on data preprocessing controlprocessor 305, and (ii) contextual environmental data from database 310b (e.g., map data and/or object data) accessed by the vehicle dynamicscontrol processor 310 (where the contextual environmental dataidentifies a known location of an environmental object, such as anaircraft, building, other GSE, and the like).

In still another example, the predicted state of the exteriorenvironment of tractor 115 may be determined by the vehicle dynamicscontrol processor 310 based upon depth information provided to thevehicle dynamics control processor 310 by the data preprocessing controlprocessor 305, where the depth information is generated by the dataprocessing control processor using the second sensor data received bythe data preprocessing control processor 305 (e.g., where such depthinformation is object detection and distance estimation informationrepresenting one or more detected environmental objects in the exteriorenvironment of the tractor 115, and where such object detection anddistance estimation information is generated by scene understandingmodule 305 b running on the data processing control processor 305 and isbased upon at least part of the second sensor data received by the datapreprocessing control processor, such as image data from camera sensor188, or radar data from radar sensor 190, multi-dimensional map datafrom LiDAR sensor 186, and/or proximity data from an ultrasonic sensor420. As explained above, exemplary depth information (e.g., objectdetection, object classification, and distance estimation information)and spatial awareness information may be generated by the dataprocessing control processor 305 using exteroceptive sensor datareceived by the data preprocessing control processor 305 and where suchdepth and spatial information is related to an environmental object inthe exterior environment of the tractor 115 (e.g., aircraft 100, arestricted ramp area for aircraft operations, a change in roadway grade,a building, a transition between buildings, and the like).

In a further example, the predicted state of the exterior environment oftractor 115 may be determined by the vehicle dynamics control processor310 based upon orientation information provided to the vehicle dynamicscontrol processor 310 by the data preprocessing control processor 305.In this example, such orientation information may supplement locationinformation on the tractor 115, and may be generated by the dataprocessing control processor 305 using the exteroceptive sensor datareceived by the data preprocessing control processor 305 (location data,visual image data, radar data, LiDAR data, and the like) that helpsidentify a relative orientation of tractor 115 to environmental objectsdetected in the exterior environment of the tractor 115 (e.g.,orientation to an aircraft, loader 110, beacon, ramp area, and thelike).

In a further embodiment of the adaptive control system for improvedengine performance on logistics ground support equipment, the system mayfurther include second proprioceptive sensor coupled to the vehicledynamics control processor that monitors a weight of cargo transportedby the logistics ground support equipment (e.g., weight sensor 196). Assuch and in this further embodiment, the control system (e.g., refitcontrol system 140) may be further programmatically configured to beoperative to (f) receive third sensor data generated by this weightsensor using the vehicle dynamics control processor 310 (where the thirdsensor data indicating the weight of the cargo transported by thelogistics ground support equipment). With this additional third sensordata (i.e., sensor data indicative of weight or load associated with thetractor 115), the control system may be programmatically configured tobe operative to perform (d) by being further operative to proactivelyadjust the throttle control 154 for the engine 142 with the adjustmentsignal supplied by the vehicle dynamics control processor 310 to thethrottle actuator 174, where the level of the adjustment signal in thisembodiment is based upon the first sensor data and the second sensordata and the third sensor data. As such, the control system may also beprogrammatically configured to be operative to perform (e) by beingfurther operative to repeat functions (a)-(d) and (f) in a modifiedversion of the adaptive feedback loop to address latency in performanceof the engine of the logistics ground support equipment.

As noted above, throttles on carbureted engines may be more sensitive totemperature and other environmental conditions when compared tonon-carbureted engines. In a further embodiment of the adaptive controlsystem for improved engine performance on a logistics ground supportequipment, such a system may further include another temperature sensortype of proprioceptive sensor (e.g., exemplary temperature sensor 194)coupled to the vehicle dynamics control processor 310 (e.g., the vehiclemotion control module 310 d running on vehicle dynamics controlprocessor 310) that monitors a temperature related to tractor 115. Inthis further embodiment, the control system 140 may be furtherprogrammatically configured to be operative to (f) receive third sensordata generated by such a temperature sensor using the vehicle dynamicscontrol processor 310. In this further embodiment, function (d) may bemodified so that control system 140 is programmatically configured to beoperative to perform (d) by proactively adjusting the throttle control154 for the engine 142 with the adjustment signal supplied by thevehicle dynamics control processor 310 to the throttle actuator 174,where the level of the adjustment signal in this embodiment may be basedupon the first sensor data (engine operating parameter data) and thesecond sensor data (sensor data from exteroceptive sensors) and thethird sensor data (temperature-related sensor data). As such, controlsystem 140 may be programmatically configured to be operative to perform(e) by being further operative to repeat functions (a)-(d) and (f) in amodified version of the adaptive feedback loop to address latency inperformance of the engine of the logistics ground support equipment. Anembodiment may be further programmatically configured for the controlsystem 140 to perform (e) within a predetermined time period to addresslatency in performance of the GSE engine, where parameters on such apredetermined time period varying according to different models of GSEand their respective engines.

The above-described embodiments for an adaptive control system may befurther described in terms of a method of operation of such an adaptivecontrol system that improves engine performance of logistics groundsupport equipment. FIG. 14 is a flow diagram of an exemplary method forimproved engine performance using an adaptive control system onlogistics ground support equipment in accordance with an embodiment ofthe invention. Referring now to FIG. 14, exemplary method 1400 begins atstep 1405 with a vehicle dynamics control processor (e.g., vehicledynamics control processor 310) receiving first sensor data generated bya first proprioceptive sensor that monitors an engine operatingparameter of the engine, where the first sensor data indicates a currentstate of the engine operating parameter. For example, such an engineoperating parameter may be a power generated by engine 142 as monitoredby an air flow sensor disposed within engine 142 as the firstproprioceptive sensor. In another example, the engine operatingparameter may be a power generated by engine 142 as monitored byexemplary engine RPM sensor 180 or an exemplary transmission statussensor 166 as the first proprioceptive sensor.

At step 1410, method 1400 continues with a data preprocessing controlprocessor (e.g., data preprocessing control processor 305) receivingsecond sensor data generated by group of exteroceptive sensors thatmonitor an exterior environment of the logistics ground supportequipment. The second sensor data indicates a current state of theexterior environment of the logistics ground support equipment. Forexample, data preprocessing control processor 305 may receive sensordata from one or more of LiDAR 186, one or more camera sensors 188,radar sensor 190, IMU 156, and GPS 158. In this example, datapreprocessing control processor 305 receives sensor data about theexterior environment of refit tractor 115—e.g., visual, radar, andmapping images of what is near tractor 115 and from which objects may bedetected, classified, and ranges (via processing modules 305 a, 305 b)and location information on the tractor 115.

At step 1415, method 1400 proceeds with the vehicle dynamics controlprocessor determining a predicted state of the exterior environmentbased upon the second sensor data. For example, exemplary vehicledynamics control process 310 may use model predictive control module 310c (or MPC) to determine the predicted state of the exterior environmentof refit tractor 115 in the near future using predictive look-aheadsteps as explained above.

In more detail, a more specific embodiment of step 1415 may have thevehicle dynamics control processor determining the predicted state ofthe exterior environment by the vehicle dynamics control processor basedupon spatial awareness information provided to the vehicle dynamicscontrol processor by the data preprocessing control processor. Suchspatial awareness information in this embodiment of step 1415 may begenerated by the data processing control processor using the secondsensor data received by the data preprocessing control processor. Forexample, vehicle dynamics control processor 310 may determine thepredicted state of the exterior environment of refit tractor 115 by thevehicle dynamics control processor 310 running MPC 310 c based uponspatial awareness information provided by spatial awareness module 305 crunning on data preprocessing control processor 305 to the vehicledynamics control processor 310. Such spatial awareness information maybe based upon location data representing a current position of the refittractor 115 (e.g., location data generated by exemplary GPS sensor 158or IMU 156 disposed on the refit tractor 115 as at least part of thegroup of exteroceptive sensors).

In another embodiment of step 1415, the vehicle dynamics controlprocessor may determine the predicted state of the exterior environmentbased upon (i) spatial awareness information provided to the vehicledynamics control processor by the data preprocessing control processor(where the spatial information is generated by the data processingcontrol processor using the second sensor data received by the datapreprocessing control processor); and (ii) contextual environmental dataaccessed by the vehicle dynamics control processor (wherein thecontextual environmental data identifies a known location of anenvironmental object). Such contextual environmental data may, forexample, include map and/or object data in database 310 b accessible byMPC 310 c running on vehicle dynamics control processor 310 as explainedrelative to FIGS. 3 and 4.

In still another embodiment of step 1415, the vehicle dynamics controlprocessor may determine the predicted state of the exterior environmentbased upon depth information provided to the vehicle dynamics controlprocessor by the data preprocessing control processor, where such depthinformation is generated by the data processing control processor usingthe second sensor data received by the data preprocessing controlprocessor. For example, vehicle dynamics control processor 310 maydetermine the predicted state of the exterior environment of refittractor 115 by the vehicle dynamics control processor 310 running MPC310 c based upon depth information provided by scene understandingmodule 305 b running on data preprocessing control processor 305 to thevehicle dynamics control processor 310. In more detail, exemplary depthinformation may be object detection and distance estimation informationrepresenting one or more detected environmental objects in the exteriorenvironment of the logistics ground support equipment, where such objectdetection and distance estimation information is generated by the dataprocessing control processor 305 running scene understanding module 305b and is based upon at least part of the second sensor data received bythe data preprocessing control processor (e.g., image data from camerasensor 188 disposed on refit tractor 115, radar data from radar sensor190 disposed on refit tractor, and/or proximity data from ultrasonicsensor 420 (or some other proximity sensor) disposed on refit tractor115).

In yet another embodiment of step 1415, the vehicle dynamics controlprocessor may determine the predicted state of the exterior environmentbased upon depth and spatial awareness information provided to thevehicle dynamics control processor by the data preprocessing controlprocessor, where the depth and spatial awareness information isgenerated by the data processing control processor using the secondsensor data received by the data preprocessing control processor, andwhere the depth and spatial information is related to an environmentalobject in the exterior environment of the logistics ground supportequipment. Such an environmental object may be, for example, anaircraft, a restricted ramp area for aircraft operations, a change inroadway grade, a building, or a transition between a first building anda second building.

In still another embodiment of step 1415, the vehicle dynamics controlprocessor may determine the predicted state of the exterior environmentbased upon orientation information provided to the vehicle dynamicscontrol processor by the data preprocessing control processor. Suchorientation information may be generated by the data processing controlprocessor using the second sensor data received by the datapreprocessing control processor, and where the orientation informationis relative to an environmental object detected in the exteriorenvironment of the logistics ground support equipment. For example,orientation information generated by data preprocessing controlprocessor 305 may be a type of spatial awareness information thatindicates a relative orientation of refit tractor 115 to anenvironmental object detected near the refit tractor 115 (e.g., a nearbyloader 110 or aircraft 100).

At step 1420, method 1400 proceeds with the vehicle dynamics controlprocessor proactively adjusting a throttle control for the engine (e.g.,throttle 154 for engine 142 on refit tractor 115) with an adjustmentsignal supplied by the vehicle dynamics control processor to a throttleactuator (e.g., throttle actuator 174), where the level of theadjustment signal is based upon the first sensor data and the secondsensor data. Thereafter, step 1420 of method 1400 moves back to step1405 so as to repeat steps 1405 through 1420 in an adaptive feedbackloop to address latency in performance of the engine of the logisticsground support equipment. In some embodiments, repetition of steps1405-1420 may be accomplished within a predetermined time period toaddress the particular latency in performance of a particular engineused in the logistics ground support equipment.

In a further embodiment, method 1400 may also include a step with thevehicle dynamics control processor receiving third sensor data generatedby a second proprioceptive sensor coupled to the vehicle dynamicscontrol processor that monitors a weight of cargo transported by thelogistics ground support equipment, where the third sensor dataindicates or is at least related to the weight of the cargo transportedby the logistics ground support equipment. As such, step 1420 may beimplemented with the vehicle dynamics control processor proactivelyadjusting the throttle control for the engine with the adjustment signalwhere the level of the adjustment signal is now based upon the firstsensor data and the second sensor data and the third sensor data.Additionally in this further embodiment, step 1420 of method 1400 movesback to step 1405 so as to repeat steps 1405, 1410, 1415, modified step1420 as well as the step of receiving the third sensor data in anadaptive feedback loop to address latency in performance of the engineof the logistics ground support equipment.

In another further embodiment, method 1400 may also include a step withthe vehicle dynamics control processor receiving third sensor datagenerated by a second proprioceptive sensor coupled to the vehicledynamics control processor that monitors a temperature related to thelogistics ground support equipment, where the third sensor dataindicates the temperature of the logistics ground support equipment(e.g., an ambient temperature on or near the refit tractor 115). Assuch, step 1420 may be implemented with the vehicle dynamics controlprocessor proactively adjusting the throttle control for the engine withthe adjustment signal where the level of the adjustment signal is nowbased upon the first sensor data and the second sensor data and thethird sensor data. Additionally in this further embodiment, step 1420 ofmethod 1400 moves back to step 1405 so as to repeat steps 1405, 1410,1415, modified step 1420 as well as the step of receiving the thirdsensor data in an adaptive feedback loop to address latency inperformance of the engine of the logistics ground support equipment.

Those skilled in the art will appreciate that this adaptive controlmethod for improving engine performance on a logistics ground supportequipment as disclosed and explained above in various embodiments may beimplemented on refit control system 140 and with particularexteroceptive sensors 230, particular proprioceptive sensors 220, and athrottle actuator 174 illustrated in FIGS. 1B-4, running program codedisposed to be executed by respective processors used in control system140. Such code may be stored on a non-transitory computer-readablemedium such as memory storage coupled with each of such processors.Thus, when executing such code, the control system may be operative toperform operations or steps from the exemplary methods disclosed above,including this method and variations of that method.

Enhanced Contextual Environment Data for Spatial Awareness

Still further embodiments may have methods and systems deployingenhanced data preprocessing and spatial awareness techniques by anexemplary refit control system via the use of the known contextualenvironment for particular logistics ground support equipment (such as acargo tug). Such a contextual environment may, for example, includeinformation on known environments (e.g., buildings, airport layout,plane footprint, and the like) as well as a temporally detectedenvironment of an airport and loading areas (e.g., temporarybarriers/temporary objects vs base layout). Further aspects of thecontextual environment may include a time component added tostatic/known layout issue (e.g., day v. night; seasonal issues;personnel detected) when navigating the logistics areas, such as anairport and/or particular loading and unloading areas.

Local optimization is the component of path optimization that finds thepath with the minimum time. Sometimes this can be based on completeknowledge of the surrounding layout of the environment (e.g., contextualdata used by the onboard controller) or based on a “guess” where pasthistory (e.g., another type of contextual data based on historicalinformation) or unfamiliar conditions may dictate a more preferredmovement based on ether human or robotic conservation (e.g., promptedcontextual data).

As noted above, embodiments may include electronics that use standardcommunicative protocols such as Bluetooth, RFID, IEEE 802.11, ZigBee,Cellular, NFC, etc. and transmit information about key components of theenvironment to one or more parts of the retrofit assembly apparatusonboard the refit GSE platform (e.g., the refit control system 140). Ingeneral, such an embodiment essentially provides a layer of contextualawareness to inanimate objects positioned around the facility and allowsthe environment to tell—in real time—the refit C-TAR-enabled equipmentwhat to do (or not do), what to know, how to interact, and so on. Thistechnical equipment that could be installed throughout a facility mayessentially utilize the same concepts as the IoT (Internet of Things)where there is a computational component, a series of various sensorsthat identify information about the environment, and a communicativebackbone (e.g., a transmitter, transceiver, and the like) that may beimplemented in hardware alone or in a combination of hardware andsoftware (e.g., software-defined radios as a type of communicativebackbone).

For example, such an embodiment may take the form of a retrofit assemblyapparatus for use on a logistics ground support equipment (e.g., a refittractor 115) to enhance a level of autonomous operation of the logisticsground support equipment. Such an assembly apparatus may include, forexample, a refit control system (e.g., control system 140) attached tothe refit tractor 115, retrofitted sensors (e.g., sensors 220, 230)coupled to the refit control system and disposed on the refit tractor115 as retrofitted equipment to the tractor 115, and actuators (e.g.,actuators 225) coupled to the refit control system. In more detail, therefit control system in this apparatus embodiment involving the use ofsuch contextual environment data includes a vehicle dynamics controlprocessor (e.g., exemplary vehicle dynamics control processor 310), adata preprocessing control processor (e.g., exemplary data preprocessingcontrol processor 305) in communication with the vehicle dynamicscontrol processor, and a database (e.g., exemplary database 310 b)operatively coupled to the vehicle dynamics control processor, where thedatabase maintains contextual environment data (e.g., map data, objectdata, and the like) related to an operating environment for thelogistics ground support equipment. In this apparatus embodiment, theretrofitted sensors include a first group of proprioceptive sensors(e.g., exemplary sensors 220) coupled to the vehicle dynamics controlprocessor 310 that monitor operating parameters and characteristics ofthe refit tractor 115, and a second group of exteroceptive sensors(e.g., exemplary sensors 230) coupled to the data preprocessing controlprocessor 305 that monitor an exterior environment of the refit tractor115. Each of the actuators (e.g., exemplary actuators 225) is disposedon refit tractor 115 as a retrofit actuator to add control of differentcontrol elements on the refit tractor 115 (such as the throttle 154,transmission 146, brake system 148, steering system 144, and the like)and to autonomously alter motion of the refit tractor 115. In thisparticular apparatus embodiment involving the use of such contextualenvironment data, the refit control system 140 is programmaticallyconfigured to be operative to receive first sensor data generated by thefirst group of proprioceptive sensors using the vehicle dynamics controlprocessor and receive second sensor data generated by the second groupof exteroceptive sensors using the data preprocessing control processor.The data preprocessing control processor 305 of the refit control system140 is programmatically configured to be operative to generate depth andspatial awareness information relative to the current state of the refittractor 115 based upon the second sensor data. The refit control system140 is further programmatically configured to access the contextualenvironment data from the database using the vehicle dynamics controlprocessor 310 (via MPC 310 c) based upon the depth and spatial awarenessinformation provided by the data preprocessing control processor 305 tothe vehicle dynamics control processor 310. The vehicle dynamics controlprocessor 310 of the refit control system 140 is programmaticallyconfigured to be further operative to (a) determine a heading and speedfor the refit tractor 115 (e.g., an example of an optimized path) basedupon the depth and spatial awareness information as provided by the datapreprocessing control processor 305 to the vehicle dynamics controlprocessor 310, the contextual environmental data accessed by the vehicledynamics control processor 310, and the first sensor data received bythe vehicle dynamics control processor 310; and (b) activate at leastone of the actuators according to the determined heading and speed forthe refit tractor 115.

In a further embodiment of this particular apparatus involving theenhanced use of contextual environment data, exemplary contextualenvironment data (as accessed and used by the refit control system 140)may be implemented as a type of contextual environment datarepresentative of static known features of relevant environmentalobjects related to the operating environment of the refit tractor 115.In more detail, exemplary contextual environment data may be informationrelated to at least one known environment object existing in a currentproximate operating environment of the logistics ground supportequipment. For example, such contextual environment data may beinformation related to a building located within the current proximateoperating environment of the logistics ground support equipment,information related to an aircraft serviced by refit cargo tractor 115and/or located within the current proximate operating environment of therefit tractor 115 (e.g., information on the type of aircraft, a layoutstatus, a physical footprint of the aircraft, a loading status of theaircraft, a desired load for the aircraft, and the like). In anotherexample, such contextual environment data may be implemented asinformation related to a geofenced boundary restriction for refittractor 115 (e.g., location coordinates defining a boundary foroperations of the refit tractor 115 or location-based zones of operationfor refit tractor 115 with particular limits on operations, such aszones 800-815).

In another embodiment of this particular apparatus involving theenhanced use of contextual environment data, exemplary contextualenvironment data (as accessed and used by the refit control system 140)may be implemented as a type of contextual environment datarepresentative of temporally detected features of relevant environmentalobjects related to the operating environment of the refit tractor 115.In more detail, exemplary contextual environment data may be related toat least one environment object temporarily existing in a currentproximate operating environment of the refit tractor 115, such as depthand spatial awareness information. Such an object may, for example, be atemporary barrier existing in the current proximate operatingenvironment of the refit tractor 115.

In still another embodiment of this particular apparatus involving theenhanced use of contextual environment data, exemplary contextualenvironment data (as accessed and used by the refit control system 140)may be implemented as a type of contextual environment data about aknown environmental object in the operating environment of the refittractor 115 with particular temporal use characteristics. In moredetail, exemplary contextual environment data may be information relatedto at least one known environment object disposed in a current proximateoperating environment of the refit tractor 115 according to a knowntemporal use. For example, such an exemplary known temporal use may be ause specified during a predetermined time-based schedule (e.g., apredetermined time of day, predetermined days of a week, a predeterminedseasonal time period, and the like).

In an additional embodiment of this particular apparatus involving theenhanced use of contextual environment data, exemplary contextualenvironment data (as accessed and used by the refit control system 140)may be implemented as a type of contextual environment data based upon apast history related to the current operating environment for refittractor 115. In more detail, exemplary contextual environment data maybe information related to historical information on the currentproximate operating environment of the refit tractor 115, such ashistoric usage information about the current proximate operatingenvironment of refit tractor 115, historic usage information on loadingcargo in the current proximate operating environment of refit tractor115, and/or historic usage information on unloading cargo in the currentproximate operating environment of the logistics ground supportequipment.

Embodiments using enhanced environmental awareness through contextualenvironment data may provide path optimization solutions (e.g.,determined headings and altered speed for a refit logistics groundsupport equipment) that may leverage retrofitted hardware and sensors asan assembly apparatus or package deployed on other existing fleetmachines and ground support equipment. Such embodiments provide afurther solution that may further leverage environmental awarenessthrough contextual environment data (similar to that explained above)provided to the enhanced/retrofitted ground support equipment, where theenvironmental awareness takes the form of contextual data on temporaryobjects/landmarks, permanent or fixed objects/landmarks, and geofencingtype of boundary information as well as information on aircraft beingloaded or unloaded (e.g., type of aircraft, changes to the aircraft, thecurrent loaded layout or status for the aircraft, what may be desired tobe loaded onto the aircraft). In such a further embodiment, the refitcontrol system 140 may be programmatically configured to be furtheroperative to receive the contextual environment data related to theoperating environment for the logistics ground support equipment (e.g.,refit tractor 115) from an external transceiver, such as a transceiverimplemented as part of a refit control system deployed on anotherlogistics ground support equipment or a central server as shown in moredetail in FIG. 15. In more detail, the external transceiver may be asensor-based transceiver disposed on another logistics ground supportequipment (e.g., the transceiver-based refit control system havingsensors similar to that shown in FIG. 3) where the contextualenvironment data received from such a sensor-based transceiver on theother logistics ground support equipment may be information detectedabout an environment object (e.g., aircraft 1515) existing in a localproximate operating environment of that other logistics ground supportequipment. In an embodiment where the external transceiver isimplemented as a server, such a server may be a central server (e.g.,server 1505 shown in FIG. 15) in communication with a fleet of groundsupport equipment (e.g., GSEs 1501 a-1505 c) in the operativeenvironment for the logistics ground support equipment (e.g., GSE 1505b, which may be considered the refit tractor 115 in an example). In thisexample using an exemplary central server as the external transceiver,the contextual environment data may be received from the central server(e.g., server 1505) by GSE 1505 b operating as refit tractor 115) wherethe contextual environment data is information detected about anenvironment object existing in a local proximate operating environmentof another logistics ground support equipment (e.g., GSE 1505 c) fromthe fleet of ground support equipment. Such central server providedcontextual environment data may, for example, be information includingtemporal spatial awareness information managed by the central server andcollected by the central server from other logistics ground supportequipment in the fleet of ground support equipment.

In more detail, additional embodiments may deploy a hive type ofmanagement for a fleet of logistics ground support equipment (such as afleet of cargo tugs). This may involve the creation of temporal spatialawareness data on a central server (based upon information provided tothe server from members of the fleet) that may be proactivelydistributed to other autonomous vehicles (e.g., refit logistics groundsupport equipment) in the fleet so that different ones of the autonomousvehicles in the fleet may be aware of operations of other members of thefleet and as a way of avoiding fleet member collisions in addition tothe advantage of making use of enhanced contextual environment dataoriginally generated by other members of the fleet.

FIG. 15 is a diagram of an exemplary fleet of logistics ground supportequipment involving an exemplary central server that may manage,collect, and distribute relevant contextual environment data to and fromlogistics ground support equipment in the fleet in accordance with anembodiment of the invention. Referring now to FIG. 15, an exemplarysystem 1500 is illustrated that includes exemplary fleet of logisticsground support equipment 1510 a-1510 c (also referred to as GSE1-GSE3,respectively) and a central server 1505 (also referred to as GSE fleetcentral server 1505). Each of logistics ground support equipment 1510a-1510 c have been retrofitted with a refit assembly apparatus asdescribed above similar to that shown in FIGS. 1B-4 and similar to thatdescribed above relative to refit tractor 115. In such a fleet-basedsystem embodiment, those skilled in the art will appreciate that each oflogistics ground support equipment 1510 a-1510 c have their respectivelocally proximate operating environment—e.g., exemplary operatingenvironment 1520 b shown for GSE2 1510 b and exemplary operatingenvironment 1520 c shown for GSE3 1510 c. Each of logistics groundsupport equipment 1510 a-1510 c may collect sensor based data (e.g.,data from the respective exteroceptive sensors 230 and exemplaryproprioceptive sensors 220) and share such sensor-based data (which mayalso be used to update contextual environment data on the respectivelocally proximate operating environment of that particular logisticsground support equipment) with central server 1505 for centralizedmanagement and sharing of such data with other logistics ground supportequipment in the fleet. Such data may further be directly shared by thelogistics ground support equipment 1510 a-1510 c originally generatingsuch data with others of the fleet directly (e.g., from GSE2 1510 b toGSE3 1510 c). In this way, temporal spatial awareness data from one ofthe refit equipment 1510 b may be proactively distributed to otherautonomous vehicles (e.g., other refit logistics ground supportequipment 1510 a, 1510 c) in the fleet directly or via uploading andfurther distribution by central server 1505.

As shown in FIG. 15, an embodiment may be implemented of an exemplarysystem to enhance a level of autonomous operation of logistics groundsupport equipment operating as a fleet of ground support equipment. Inthis embodiment, the exemplary system may include an external managementtransceiver (e.g., central server 1505) in communication with a first ofthe logistics ground support equipment (e.g., refit GSE2 1510 b) and asecond of the logistics ground support equipment (e.g., refit GSE3 1510c). The exemplary system further includes a refit control system 140attached to the first logistics ground support equipment (e.g., refitGSE2 1510 b similar to that described above for refit tractor 115). Sucha refit control system in this embodiment includes a vehicle dynamicscontrol processor (e.g., exemplary vehicle dynamics control processor310), a data preprocessing control processor (e.g., exemplary datapreprocessing control processor 305) in communication with the vehicledynamics control processor, and a database (e.g., exemplary database 310b) operatively coupled to the vehicle dynamics control processor. Theexemplary system further includes retrofitted sensors (e.g., exemplarysensors 205) and actuators (e.g., exemplary actuators 225) coupled tothe refit control system. Such retrofitted sensors in this systemembodiment are disposed on refit GSE2 1510 b as retrofitted equipment torefit GSE2 1510 b and include a first group of proprioceptive sensorscoupled to the vehicle dynamics control processor on refit GSE2 1510 bthat monitor operating parameters and characteristics of refit GSE2 1510b, and a second group of exteroceptive sensors coupled to the datapreprocessing control processor on refit GSE2 1510 b that monitor anexterior environment of refit GSE2 1510 b. The actuators coupled to therefit control system on refit GSE2 1510 b and disposed on refit GSE21510 b add control of different ones of control elements (e.g.,steering, throttle, transmission, brakes) on refit GSE2 1510 b and toautonomously alter motion of refit GSE2 1510 b.

In exemplary embodiment, the system's refit control system on refit GSE21510 b is programmatically configured to be operative to receivecontextual environment data from the external management transceiver,where the contextual environment data is related to an operatingenvironment for the second logistics ground support equipment (e.g.,refit GSE3 1510 c) and store the received contextual environment data inthe database 310 b on refit GSE2 1510 b using the vehicle dynamicscontrol processor 310 on refit GSE2 1510 b. The system's refit controlsystem on refit GSE2 1510 b is further programmatically configured to beoperative to receive first sensor data generated by the first group ofproprioceptive sensors using the vehicle dynamics control processor 305of the refit control system on refit GSE2 1510 b, receive second sensordata generated by the second group of exteroceptive sensors using thedata preprocessing control processor 305 on refit GSE2 1510 b, andgenerate depth and spatial awareness information relative to the currentstate of refit GSE2 1510 b based upon the second sensor data using thedata preprocessing control processor 305 on refit GSE2 1510 b. Thesystem's refit control system on refit GSE2 1510 b is furtherprogrammatically configured to be operative to determine a heading andspeed for refit GSE2 1510 b (as the first logistics ground supportequipment) by the vehicle dynamics control processor 310 on refit GSE21510 b based upon the depth and spatial awareness information asprovided by the data preprocessing control processor 305 to the vehicledynamics control processor 310, the received contextual environmentaldata related to the operating environment for the second logisticsground support equipment, and the first sensor data received by thevehicle dynamics control processor 310. The system's refit controlsystem on refit GSE2 1510 b is then further programmatically configuredto be operative to activate (using the vehicle dynamics controlprocessor 310 on refit GSE2 15010 b) at least one of the actuatorsaccording to the determined heading and speed for refit GSE2 15010 b asthe first logistics ground support equipment.

In a further embodiment, the system may use locally maintainedcontextual environment data as well as contextual environment datareceived from the external management transceiver. For example, thesystem's refit control system may be programmatically configured in thisfurther embodiment to be operative to access local contextualenvironment data from the database 310 b using the vehicle dynamicscontrol processor 310 on refit GSE2 1510 b based upon the depth andspatial awareness information provided by the data preprocessing controlprocessor 305 to the vehicle dynamics control processor 310; anddetermine the heading and speed for refit GSE2 1510 b (as the firstlogistics ground support equipment) by the vehicle dynamics controlprocessor 310 on refit GSE2 1510 b based upon the depth and spatialawareness information as provided by the data preprocessing controlprocessor 305 to the vehicle dynamics control processor 310 on refitGSE2 1510 b, the received contextual environmental data related to theoperating environment for the second logistics ground support equipment,the local contextual environmental data accessed by the vehicle dynamicscontrol processor 310 on refit GSE2 1510 b, and the first sensor datareceived by the vehicle dynamics control processor 310 on refit GSE21510 b.

In a more detailed embodiment, the received contextual environment datamay be information related to at least one known environment objectexisting in a current proximate operating environment of the secondlogistics ground support equipment and in a current proximate operatingenvironment of refit GSE2 1510 b (e.g., exemplary aircraft 1515 shown inFIG. 15). Further examples of such received contextual environment datamay include, for example, information related to a building locatedwithin in a current proximate operating environment of the secondlogistics ground support equipment and in a current proximate operatingenvironment of refit GSE2 1510 b; a layout status of an aircraftserviced by the first logistics ground support equipment and the secondlogistics ground support equipment; a physical footprint of an aircraftserviced by the first logistics ground support equipment and the secondlogistics ground support equipment; a loading status of an aircraftserviced by the first logistics ground support equipment and the secondlogistics ground support equipment; a desired load for an aircraftserviced by the first logistics ground support equipment and the secondlogistics ground support equipment; a type of an aircraft serviced bythe first logistics ground support equipment and the second logisticsground support equipment; and a geofenced boundary restriction for thefirst logistics ground support equipment and the second logistics groundsupport equipment.

In another example, the received contextual environment data may beinformation related to at least one environment object temporarilyexisting in a current proximate operating environment of the secondlogistics ground support equipment (e.g., refit GSE3 1510 c) and in acurrent proximate operating environment of the first logistics groundsupport equipment (e.g., refit GSE2 1510 b). In more detail, suchreceived contextual environment data on the temporary environment objectmay be related to the depth and spatial awareness information for thetemporary environment object. An example of such a temporary environmentobject may be a temporary barrier (e.g., a traffic cone, trafficbarrier, temporary fence, and the like) existing in the currentproximate operating environment of the first logistics ground supportequipment and in the current proximate operating environment of thesecond logistics ground support equipment.

In still another exemplary embodiment, the received contextualenvironment data may be information related to at least one knownenvironment object disposed in a current proximate operating environmentof the second logistics ground support equipment and in a currentproximate operating environment of the first logistics ground supportequipment according to a known temporal use. For example, such a knowntemporal use may include a use specified during a predeterminedtime-based schedule, a predetermined time of day, one or morepredetermined days of a week, and/or a use specified during apredetermined seasonal time period.

A further example may have the received contextual environment databeing implemented with historical information on a current proximateoperating environment of the second logistics ground support equipmentcommon with a current proximate operating environment of the firstlogistics ground support equipment. For example, such historicalinformation may be historic usage information about the currentproximate operating environment of the second logistics ground supportequipment common with the current proximate operating environment of thefirst logistics ground support equipment, such as historic usageinformation on loading or unloading cargo in the current proximateoperating environment of the second logistics ground support equipmentcommon with the current proximate operating environment of the firstlogistics ground support equipment.

The system embodiments described above involving received contextualenvironment data may, for example, implement the external managementtransceiver as a sensor-based transceiver (such as a sensor-basedtransceiver disposed on refit GSE1 1510 a). Other embodiments mayimplement the external management transceiver as a central server, suchas GSE fleet central server 1505 in communication with a fleet of groundsupport equipment 1510 a-1510 c in the operative environment for thelogistics ground support equipment. The contextual environment dataprovided by such a central server may be temporal spatial awarenessinformation managed by the central server and collected by the centralserver from each of the logistics ground support equipment 1510 a-1510 cin the fleet of ground support equipment.

What follows is a further collective description of differentembodiments consistent with and exemplified by the above description.

Further Embodiments (Set B)—Feedback Loop Monitoring

1. An adaptive control system for improved engine performance on alogistics ground support equipment having at least an engine and athrottle control for the engine, the assembly comprising:

a control system attached to the logistics ground support equipment, thecontrol system comprising a vehicle dynamics control processor and adata preprocessing control processor;

a plurality of sensors coupled to the control system and disposed on thelogistics ground support equipment, the retrofitted sensors comprising

a first proprioceptive sensor coupled to the vehicle dynamics controlprocessor that monitors an engine operating parameter of the engine ofthe logistics ground support equipment, and

a second group of exteroceptive sensors coupled to the datapreprocessing control processor that monitor an exterior environment ofthe logistics ground support equipment;

a throttle actuator coupled to the vehicle dynamics control processor ofthe control system and disposed on the logistics ground supportequipment to be in responsive communication with the throttle controlfor the engine;

wherein the control system is programmatically configured to beoperative to

(a) receive first sensor data generated by the first proprioceptivesensor using the vehicle dynamics control processor, the first sensordata indicating a current state of the engine operating parameter,

(b) receive second sensor data generated by the second group ofexteroceptive sensors using the data preprocessing control processor,the second sensor data indicating a current state of the exteriorenvironment of the logistics ground support equipment,

(c) determine, by the vehicle dynamics control processor, a predictedstate of the exterior environment based upon the second sensor data,

(d) proactively adjust the throttle control for the engine with anadjustment signal supplied by the vehicle dynamics control processor tothe throttle actuator, wherein the level of the adjustment signal isbased upon the first sensor data and the second sensor data, and

(e) repeat functions (a)-(d) in an adaptive feedback loop to addresslatency in performance of the engine of the logistics ground supportequipment.

2. The system of embodiment 1, wherein the engine operating parameter ofthe engine comprises a power generated by the engine as monitored by anair flow sensor as the first proprioceptive sensor.

3. The system of embodiment 1, wherein the engine operating parameter ofthe engine comprises a power generated by the engine as monitored by anengine RPM sensor as the first proprioceptive sensor.

4. The system of embodiment 1, wherein the engine operating parameter ofthe engine comprises a power generated by the engine as monitored by atransmission status sensor as the first proprioceptive sensor.

5. The system of embodiment 1, wherein the predicted state of theexterior environment is determined by the vehicle dynamics controlprocessor based upon spatial awareness information provided to thevehicle dynamics control processor by the data preprocessing controlprocessor, the spatial awareness information being generated by the dataprocessing control processor using the second sensor data received bythe data preprocessing control processor.

6. The system of embodiment 5, wherein the spatial awareness informationprovided to the vehicle dynamics control processor by the datapreprocessing control processor is based upon location data representinga current position of the logistics ground support equipment as at leastpart of the second sensor data received by the data preprocessingcontrol processor.

7. The system of embodiment 6, wherein the location data being generatedby a GPS sensor disposed on the logistics ground support equipment as atleast part of the second group of exteroceptive sensors.

8. The system of embodiment 6, wherein the location data being generatedby an inertial measurement unit (IMU) sensor disposed on the logisticsground support equipment as at least part of the second group ofexteroceptive sensors.

9. The system of embodiment 6, wherein the predicted state of theexterior environment is determined by the vehicle dynamics controlprocessor based upon

(i) spatial awareness information provided to the vehicle dynamicscontrol processor by the data preprocessing control processor, thespatial information being generated by the data processing controlprocessor using the second sensor data received by the datapreprocessing control processor; and

(ii) contextual environmental data accessed by the vehicle dynamicscontrol processor, wherein the contextual environmental data identifiesa known location of an environmental object.

10. The system of embodiment 1, wherein the predicted state of theexterior environment is determined by the vehicle dynamics controlprocessor based upon depth information provided to the vehicle dynamicscontrol processor by the data preprocessing control processor, the depthinformation being generated by the data processing control processorusing the second sensor data received by the data preprocessing controlprocessor.

11. The system of embodiment 10, wherein the depth information providedto the vehicle dynamics control processor by the data preprocessingcontrol processor comprises object detection and distance estimationinformation representing one or more detected environmental objects inthe exterior environment of the logistics ground support equipment, theobject detection and distance estimation information being generated bythe data processing control processor based upon at least part of thesecond sensor data received by the data preprocessing control processor.

12. The system of embodiment 11, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon image data from a camerasensor disposed on the logistics ground support equipment as at leastpart of the second group of exteroceptive sensors.

13. The system of embodiment 11, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon radar data from a radarsensor disposed on the logistics ground support equipment as at leastpart of the second group of exteroceptive sensors.

14. The system of embodiment 11, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon multi-dimensional map datafrom a LiDAR sensor disposed on the logistics ground support equipmentas at least part of the second group of exteroceptive sensors.

15. The system of embodiment 11, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon proximity data from anultrasonic sensor disposed on the logistics ground support equipment asat least part of the second group of exteroceptive sensors.

16. The system of embodiment 1, wherein the predicted state of theexterior environment is determined by the vehicle dynamics controlprocessor based upon depth and spatial awareness information provided tothe vehicle dynamics control processor by the data preprocessing controlprocessor, the depth and spatial awareness information being generatedby the data processing control processor using the second sensor datareceived by the data preprocessing control processor, the depth andspatial information being related to an environmental object in theexterior environment of the logistics ground support equipment.

17. The system of embodiment 16, wherein the environmental objectcomprises an aircraft.

18. The system of embodiment 16, wherein the environmental objectcomprises a restricted ramp area for aircraft operations.

19. The system of embodiment 16, wherein the environmental objectcomprises a change in roadway grade.

21. The system of embodiment 16, wherein the environmental objectcomprises a building.

22. The system of embodiment 16, wherein the environmental objectcomprises a transition between a first building and a second building.

23. The system of embodiment 1, wherein the predicted state of theexterior environment is determined by the vehicle dynamics controlprocessor based upon orientation information provided to the vehicledynamics control processor by the data preprocessing control processor,the orientation information being generated by the data processingcontrol processor using the second sensor data received by the datapreprocessing control processor, the orientation information beingrelative to an environmental object detected in the exterior environmentof the logistics ground support equipment.

24. The system of embodiment 1 further comprising a secondproprioceptive sensor coupled to the vehicle dynamics control processorthat monitors a weight of cargo transported by the logistics groundsupport equipment;

wherein the control system is further programmatically configured to beoperative to (f) receive third sensor data generated by the secondproprioceptive sensor using the vehicle dynamics control processor, thethird sensor data indicating the weight of the cargo transported by thelogistics ground support equipment;

wherein the control system is programmatically configured to beoperative to perform (d) by being further operative to proactivelyadjust the throttle control for the engine with the adjustment signalsupplied by the vehicle dynamics control processor to the throttleactuator, wherein the level of the adjustment signal being based uponthe first sensor data and the second sensor data and the third sensordata; and

wherein the control system is programmatically configured to beoperative to perform (e) by being further operative to repeat functions(a)-(d) and (f) in the adaptive feedback loop to address latency inperformance of the engine of the logistics ground support equipment.

25. The system of embodiment 1 further comprising a secondproprioceptive sensor coupled to the vehicle dynamics control processorthat monitors a temperature related to the logistics ground supportequipment;

wherein the control system is further programmatically configured to beoperative to (f) receive third sensor data generated by the secondproprioceptive sensor using the vehicle dynamics control processor, thethird sensor data indicating the temperature related to the logisticsground support equipment;

wherein the control system is programmatically configured to beoperative to perform (d) by being further operative to proactivelyadjust the throttle control for the engine with the adjustment signalsupplied by the vehicle dynamics control processor to the throttleactuator, wherein the level of the adjustment signal being based uponthe first sensor data and the second sensor data and the third sensordata; and

wherein the control system is programmatically configured to beoperative to perform (e) by being further operative to repeat functions(a)-(d) and (f) in the adaptive feedback loop to address latency inperformance of the engine of the logistics ground support equipment.

26. The system of embodiment 1, wherein the control system is furtherprogrammatically configured to be operative to perform (e) by beingoperative to repeat functions (a)-(d) in the adaptive feedback loopwithin a predetermined time period to address latency in performance ofthe engine of the logistics ground support equipment.

27. A method for improved engine performance using an adaptive controlsystem on a logistics ground support equipment having at least an engineand a throttle control for the engine, the method comprising the stepsof:

(a) receiving, by a vehicle dynamics control processor, first sensordata generated by a first proprioceptive sensor that monitors an engineoperating parameter of the engine, the first sensor data indicating acurrent state of the engine operating parameter;

(b) receiving, by a data preprocessing control processor, second sensordata generated by a second group of exteroceptive sensors that monitoran exterior environment of the logistics ground support equipment, thesecond sensor data indicating a current state of the exteriorenvironment of the logistics ground support equipment;

(c) determining, by the vehicle dynamics control processor, a predictedstate of the exterior environment based upon the second sensor data;

(d) proactively adjusting, by the vehicle dynamics control processor, athrottle control for the engine with an adjustment signal supplied bythe vehicle dynamics control processor to a throttle actuator, whereinthe level of the adjustment signal is based upon the first sensor dataand the second sensor data; and

(e) repeating steps (a)-(d) in an adaptive feedback loop to addresslatency in performance of the engine of the logistics ground supportequipment.

28. The method of embodiment 27, wherein the engine operating parameterof the engine comprises a power generated by the engine as monitored byan air flow sensor as the first proprioceptive sensor.

29. The method of embodiment 27, wherein the engine operating parameterof the engine comprises a power generated by the engine as monitored byan engine RPM sensor as the first proprioceptive sensor.

30. The method of embodiment 27, wherein the engine operating parameterof the engine comprises a power generated by the engine as monitored bya transmission status sensor as the first proprioceptive sensor.

31. The method of embodiment 27, wherein step (c) comprises determiningthe predicted state of the exterior environment by the vehicle dynamicscontrol processor based upon spatial awareness information provided tothe vehicle dynamics control processor by the data preprocessing controlprocessor, the spatial awareness information being generated by the dataprocessing control processor using the second sensor data received bythe data preprocessing control processor.

32. The method of embodiment 31, wherein the spatial awarenessinformation provided to the vehicle dynamics control processor by thedata preprocessing control processor is based upon location datarepresenting a current position of the logistics ground supportequipment as at least part of the second sensor data received by thedata preprocessing control processor.

33. The method of embodiment 32, wherein the location data beinggenerated by a GPS sensor disposed on the logistics ground supportequipment as at least part of the second group of exteroceptive sensors.

34. The method of embodiment 32, wherein the location data beinggenerated by an inertial measurement unit (IMU) sensor disposed on thelogistics ground support equipment as at least part of the second groupof exteroceptive sensors.

35. The method of embodiment 32, wherein step (c) comprises determiningthe predicted state of the exterior environment by the vehicle dynamicscontrol processor based upon

(i) spatial awareness information provided to the vehicle dynamicscontrol processor by the data preprocessing control processor, thespatial information being generated by the data processing controlprocessor using the second sensor data received by the datapreprocessing control processor; and

(ii) contextual environmental data accessed by the vehicle dynamicscontrol processor, wherein the contextual environmental data identifiesa known location of an environmental object.

36. The method of embodiment 27, wherein step (c) comprises determiningthe predicted state of the exterior environment by the vehicle dynamicscontrol processor based upon depth information provided to the vehicledynamics control processor by the data preprocessing control processor,the depth information being generated by the data processing controlprocessor using the second sensor data received by the datapreprocessing control processor.

37. The method of embodiment 36, wherein the depth information providedto the vehicle dynamics control processor by the data preprocessingcontrol processor comprises object detection and distance estimationinformation representing one or more detected environmental objects inthe exterior environment of the logistics ground support equipment, theobject detection and distance estimation information being generated bythe data processing control processor based upon at least part of thesecond sensor data received by the data preprocessing control processor.

38. The method of embodiment 37, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon image data from a camerasensor disposed on the logistics ground support equipment as at leastpart of the second group of exteroceptive sensors.

39. The method of embodiment 37, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon radar data from a radarsensor disposed on the logistics ground support equipment as at leastpart of the second group of exteroceptive sensors.

40. The method of embodiment 37, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon multi-dimensional map datafrom a LiDAR sensor disposed on the logistics ground support equipmentas at least part of the second group of exteroceptive sensors.

41. The method of embodiment 37, wherein the object detection anddistance estimation information being generated by the datapreprocessing control processor based upon proximity data from anultrasonic sensor disposed on the logistics ground support equipment asat least part of the second group of exteroceptive sensors.

42. The method of embodiment 27, wherein step (c) comprises determiningthe predicted state of the exterior environment by the vehicle dynamicscontrol processor based upon depth and spatial awareness informationprovided to the vehicle dynamics control processor by the datapreprocessing control processor, the depth and spatial awarenessinformation being generated by the data processing control processorusing the second sensor data received by the data preprocessing controlprocessor, the depth and spatial information being related to anenvironmental object in the exterior environment of the logistics groundsupport equipment.

43. The method of embodiment 42, wherein the environmental objectcomprises an aircraft.

44. The method of embodiment 42, wherein the environmental objectcomprises a restricted ramp area for aircraft operations.

45. The method of embodiment 42, wherein the environmental objectcomprises a change in roadway grade.

46. The method of embodiment 42, wherein the environmental objectcomprises a building.

47. The method of embodiment 42, wherein the environmental objectcomprises a transition between a first building and a second building.

48. The method of embodiment 27, wherein step (c) comprises determiningthe predicted state of the exterior environment by the vehicle dynamicscontrol processor based upon orientation information provided to thevehicle dynamics control processor by the data preprocessing controlprocessor, the orientation information being generated by the dataprocessing control processor using the second sensor data received bythe data preprocessing control processor, the orientation informationbeing relative to an environmental object detected in the exteriorenvironment of the logistics ground support equipment.

49. The method of embodiment 27 further comprising the step of (f)receiving, by the vehicle dynamics control processor, third sensor datagenerated by a second proprioceptive sensor coupled to the vehicledynamics control processor that monitors a weight of cargo transportedby the logistics ground support equipment, the third sensor dataindicating the weight of the cargo transported by the logistics groundsupport equipment;

wherein step (d) comprises proactively adjusting, by the vehicledynamics control processor, the throttle control for the engine with theadjustment signal supplied by the vehicle dynamics control processor tothe throttle actuator, wherein the level of the adjustment signal beingbased upon the first sensor data and the second sensor data and thethird sensor data; and

wherein step (e) comprises repeating steps (a)-(d) and (f) in theadaptive feedback loop to address latency in performance of the engineof the logistics ground support equipment.

50. The method of embodiment 27 further comprising the step (f)receiving, by the vehicle dynamics control processor, third sensor datagenerated by a second proprioceptive sensor coupled to the vehicledynamics control processor that monitors a temperature related to thelogistics ground support equipment, the third sensor data indicating thetemperature related to the logistics ground support equipment;

wherein step (d) comprises proactively adjusting, by the vehicledynamics control processor, the throttle control for the engine with theadjustment signal supplied by the vehicle dynamics control processor tothe throttle actuator, wherein the level of the adjustment signal beingbased upon the first sensor data and the second sensor data and thethird sensor data; and

wherein step (e) comprises repeating steps (a)-(d) and (f) in theadaptive feedback loop to address latency in performance of the engineof the logistics ground support equipment.

51. The method of embodiment 27, wherein step (e) comprises repeatingfunctions (a)-(d) in the adaptive feedback loop within a predeterminedtime period to address latency in performance of the engine of thelogistics ground support equipment.

Further Embodiments (Set C)—Data Preprocessing and Spatial Awareness

1. A retrofit assembly apparatus for use on a logistics ground supportequipment to enhance a level of autonomous operation of the logisticsground support equipment, the assembly comprising:

a refit control system attached to the logistics ground supportequipment, the refit control system comprising

a vehicle dynamics control processor,

a data preprocessing control processor in communication with the vehicledynamics control processor, and

a database operatively coupled to the vehicle dynamics controlprocessor, the database maintaining contextual environment data relatedto an operating environment for the logistics ground support equipment;

a plurality of retrofitted sensors coupled to the refit control systemand disposed on the logistics ground support equipment as retrofittedequipment to the logistics ground support equipment, the retrofittedsensors comprising

a first group of proprioceptive sensors coupled to the vehicle dynamicscontrol processor that monitor operating parameters and characteristicsof the logistics ground support equipment, and

a second group of exteroceptive sensors coupled to the datapreprocessing control processor that monitor an exterior environment ofthe logistics ground support equipment;

a plurality of actuators coupled to the refit control system, whereineach of the actuators being disposed on the logistics ground supportequipment as a retrofit actuator to add control of different ones of aplurality of control elements on the logistics ground support equipmentand to autonomously alter motion of the logistics ground supportequipment; and

wherein the refit control system is programmatically configured to beoperative to

-   -   receive first sensor data generated by the first group of        proprioceptive sensors using the vehicle dynamics control        processor of the refit control system,    -   receive second sensor data generated by the second group of        exteroceptive sensors using the data preprocessing control        processor,    -   generate depth and spatial awareness information relative to the        current state of the logistics ground support equipment based        upon the second sensor data using the data preprocessing control        processor,    -   access the contextual environment data from the database using        the vehicle dynamics control processor based upon the depth and        spatial awareness information provided by the data preprocessing        control processor to the vehicle dynamics control processor,    -   determine a heading and speed for the logistics ground support        equipment by the vehicle dynamics control processor based upon        the depth and spatial awareness information as provided by the        data preprocessing control processor to the vehicle dynamics        control processor, the contextual environmental data accessed by        the vehicle dynamics control processor, and the first sensor        data received by the vehicle dynamics control processor, and    -   activate, by the vehicle dynamics control processor, at least        one of the actuators according to the determined heading and        speed for the logistics ground support equipment.

2. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to at least one known environmentobject existing in a current proximate operating environment of thelogistics ground support equipment.

3. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to a building located within acurrent proximate operating environment of the logistics ground supportequipment.

4. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to a layout status of an aircraftserviced by the logistics ground support equipment.

5. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to a physical footprint of anaircraft serviced by the logistics ground support equipment.

6. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to a loading status of an aircraftserviced by the logistics ground support equipment.

7. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to a desired load for an aircraftserviced by the logistics ground support equipment.

8. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to a type of an aircraft serviced bythe logistics ground support equipment.

9. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to a geofenced boundary restrictionfor the logistics ground support equipment.

10. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to at least one environment objecttemporarily existing in a current proximate operating environment of thelogistics ground support equipment.

11. The apparatus of embodiment 10, wherein the at least one environmentobject is related to the depth and spatial awareness information.

12. The apparatus of embodiment 10, wherein the at least one environmentobject comprises a temporary barrier existing in the current proximateoperating environment of the logistics ground support equipment.

13. The apparatus of embodiment 1, wherein the contextual environmentdata comprises information related to at least one known environmentobject disposed in a current proximate operating environment of thelogistics ground support equipment according to a known temporal use.

14. The apparatus of embodiment 13, wherein the known temporal usecomprises a use specified during a predetermined time-based schedule.

15. The apparatus of embodiment 14, wherein the predetermined time-basedschedule comprises a use specified during a predetermined time of day.

16. The apparatus of embodiment 14, wherein the predetermined time-basedschedule comprises a use specified during one or more predetermined daysof a week.

17. The apparatus of embodiment 14, wherein the predetermined time-basedschedule comprises a use specified during a predetermined seasonal timeperiod.

18. The apparatus of embodiment 1, wherein the contextual environmentdata comprises historical information on a current proximate operatingenvironment of the logistics ground support equipment.

19. The apparatus of embodiment 18, wherein the historical informationcomprises historic usage information about the current proximateoperating environment of the logistics ground support equipment.

20. The apparatus of embodiment 18, wherein the historical informationcomprises historic usage information on loading cargo in the currentproximate operating environment of the logistics ground supportequipment.

21. The apparatus of embodiment 18, wherein the historical informationcomprises historic usage information on unloading cargo in the currentproximate operating environment of the logistics ground supportequipment.

22. The apparatus of embodiment 1, wherein the refit control system isprogrammatically configured to be further operative to receive thecontextual environment data related to the operating environment for thelogistics ground support equipment from an external transceiver.

23. The apparatus of embodiment 22, wherein the external transceivercomprises a sensor-based transceiver.

24. The apparatus of embodiment 23, wherein the sensor-based transceiveris disposed on another logistics ground support equipment.

25. The apparatus of embodiment 24, wherein the contextual environmentdata received from the sensor-based transceiver on the another logisticsground support equipment comprises information detected about anenvironment object existing in a local proximate operating environmentof the another logistics ground support equipment.

26. The apparatus of embodiment 22, wherein the external transceivercomprises a central server in communication with a fleet of groundsupport equipment in the operative environment for the logistics groundsupport equipment.

27. The apparatus of embodiment 26, wherein the contextual environmentdata received from the central server comprises information detectedabout an environment object existing in a local proximate operatingenvironment of another logistics ground support equipment from the fleetof ground support equipment.

28. The apparatus of embodiment 27, wherein the contextual environmentdata received from the central server comprises temporal spatialawareness information managed by the central server and collected by thecentral server from at least the another logistics ground supportequipment in the fleet of ground support equipment.

Further Embodiments (Set D)—Systems Using Contextual Environment DataReceived from an External Transceiver

1. A system to enhance a level of autonomous operation of a plurality oflogistics ground support equipment operating as a fleet of groundsupport equipment, the system comprising:

an external management transceiver in communication with a first of thelogistics ground support equipment and a second of the logistics groundsupport equipment;

a refit control system attached to the first logistics ground supportequipment, the refit control system comprising

-   -   a vehicle dynamics control processor,    -   a data preprocessing control processor in communication with the        vehicle dynamics control processor, and    -   a database operatively coupled to the vehicle dynamics control        processor;

a plurality of retrofitted sensors coupled to the refit control systemand disposed on the first logistics ground support equipment asretrofitted equipment to the first logistics ground support equipment,the retrofitted sensors comprising

-   -   a first group of proprioceptive sensors coupled to the vehicle        dynamics control processor that monitor operating parameters and        characteristics of the first logistics ground support equipment,        and    -   a second group of exteroceptive sensors coupled to the data        preprocessing control processor that monitor an exterior        environment of the first logistics ground support equipment;

a plurality of actuators coupled to the refit control system, whereineach of the actuators being disposed on the first of the logisticsground support equipment as a retrofit actuator to add control ofdifferent ones of a plurality of control elements on the first logisticsground support equipment and to autonomously alter motion of the firstof the logistics ground support equipment; and

wherein the refit control system is programmatically configured to beoperative to

-   -   receive contextual environment data from the external management        transceiver, the contextual environment data being related to an        operating environment for the second logistics ground support        equipment,    -   store the received contextual environment data in the database        using the vehicle dynamics control processor,    -   receive first sensor data generated by the first group of        proprioceptive sensors using the vehicle dynamics control        processor of the refit control system,    -   receive second sensor data generated by the second group of        exteroceptive sensors using the data preprocessing control        processor,    -   generate depth and spatial awareness information relative to the        current state of the first logistics ground support equipment        based upon the second sensor data using the data preprocessing        control processor,    -   determine a heading and speed for the first logistics ground        support equipment by the vehicle dynamics control processor        based upon the depth and spatial awareness information as        provided by the data preprocessing control processor to the        vehicle dynamics control processor, the received contextual        environmental data related to the operating environment for the        second logistics ground support equipment, and the first sensor        data received by the vehicle dynamics control processor, and    -   activate, by the vehicle dynamics control processor, at least        one of the actuators according to the determined heading and        speed for the first logistics ground support equipment.

2. The system of embodiment 1, wherein the refit control system isprogrammatically configured to be further operative to

access local contextual environment data from the database using thevehicle dynamics control processor based upon the depth and spatialawareness information provided by the data preprocessing controlprocessor to the vehicle dynamics control processor; and

determine the heading and speed for the first logistics ground supportequipment by the vehicle dynamics control processor based upon the depthand spatial awareness information as provided by the data preprocessingcontrol processor to the vehicle dynamics control processor, thereceived contextual environmental data related to the operatingenvironment for the second logistics ground support equipment, the localcontextual environmental data accessed by the vehicle dynamics controlprocessor, and the first sensor data received by the vehicle dynamicscontrol processor.

3. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to at least one knownenvironment object existing in a current proximate operating environmentof the second logistics ground support equipment and in a currentproximate operating environment of the first logistics ground supportequipment.

4. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to a building locatedwithin in a current proximate operating environment of the secondlogistics ground support equipment and in a current proximate operatingenvironment of the first logistics ground support equipment.

5. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to a layout status of anaircraft serviced by the first logistics ground support equipment andthe second logistics ground support equipment.

6. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to a physical footprintof an aircraft serviced by the first logistics ground support equipmentand the second logistics ground support equipment.

7. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to a loading status of anaircraft serviced by the first logistics ground support equipment andthe second logistics ground support equipment.

8. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to a desired load for anaircraft serviced by the first logistics ground support equipment andthe second logistics ground support equipment.

9. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to a type of an aircraftserviced by the first logistics ground support equipment and the secondlogistics ground support equipment.

10. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to a geofenced boundaryrestriction for the first logistics ground support equipment and thesecond logistics ground support equipment.

11. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to at least oneenvironment object temporarily existing in a current proximate operatingenvironment of the second logistics ground support equipment and in acurrent proximate operating environment of the first logistics groundsupport equipment.

12. The system of embodiment 11, wherein the at least one environmentobject is related to the depth and spatial awareness information.

13. The system of embodiment 11, wherein the at least one environmentobject comprises a temporary barrier existing in the current proximateoperating environment of the first logistics ground support equipmentand in the current proximate operating environment of the secondlogistics ground support equipment

14. The system of embodiment 1, wherein the received contextualenvironment data comprises information related to at least one knownenvironment object disposed in a current proximate operating environmentof the second logistics ground support equipment and in a currentproximate operating environment of the first logistics ground supportequipment according to a known temporal use.

15. The system of embodiment 14, wherein the known temporal usecomprises a use specified during a predetermined time-based schedule.

16. The system of embodiment 15, wherein the predetermined time-basedschedule comprises a use specified during a predetermined time of day.

17. The system of embodiment 15, wherein the predetermined time-basedschedule comprises a use specified during one or more predetermined daysof a week.

18. The system of embodiment 15, wherein the predetermined time-basedschedule comprises a use specified during a predetermined seasonal timeperiod.

19. The system of embodiment 1, wherein the received contextualenvironment data comprises historical information on a current proximateoperating environment of the second logistics ground support equipmentcommon with a current proximate operating environment of the firstlogistics ground support equipment.

20. The system of embodiment 19, wherein the historical informationcomprises historic usage information about the current proximateoperating environment of the second logistics ground support equipmentcommon with the current proximate operating environment of the firstlogistics ground support equipment.

21. The system of embodiment 19, wherein the historical informationcomprises historic usage information on loading cargo in the currentproximate operating environment of the second logistics ground supportequipment common with the current proximate operating environment of thefirst logistics ground support equipment.

22. The system of embodiment 19, wherein the historical informationcomprises historic usage information on unloading cargo in the currentproximate operating environment of the second logistics ground supportequipment common with the current proximate operating environment of thefirst logistics ground support equipment.

23. The system of embodiment 1, wherein the external managementtransceiver comprises a sensor-based transceiver.

24. The system of embodiment 23, wherein the sensor-based transceiver isdisposed on a third of the logistics ground support equipment.

25. The system of embodiment 1, wherein the external managementtransceiver comprises a central server in communication with a fleet ofground support equipment in the operative environment for the firstlogistics ground support equipment.

26. The system of embodiment 25, the received contextual environmentdata received from the central server comprises temporal spatialawareness information managed by the central server and collected by thecentral server from each of the logistics ground support equipment inthe fleet of ground support equipment.

Those skilled in the art will appreciate the method or processembodiments as disclosed and explained above may be implemented with anapparatus or system and implemented with the above-described vehicles,ground support equipment, suite of sensors, different processormodules/controller modules, and the different software modules runningon the different processor/controller modules as described above. Suchsoftware modules may be stored on non-transitory computer-readablemedium in each of the processor/controller modules. Thus, when executingsuch software modules, the collective processor/controller modules ofthe enhanced system for improved operations for logistics ground supportequipment that may be operative to perform the operations of theexemplary apparatus and systems disclosed above or steps from theexemplary methods disclosed above, including variations of the differentmethods, apparatus, and systems.

In summary, it should be emphasized that the sequence of operations toperform any of the methods and variations of the methods described inthe embodiments herein are merely exemplary, and that a variety ofsequences of operations may be followed while still being true and inaccordance with the principles of the present invention as understood byone skilled in the art.

At least some portions of exemplary embodiments outlined above may beused in association with portions of other exemplary embodiments toenhance and improve logistics operations (such as cargo and packageloading, transport, and unloading) using an enhanced industrial vehicleas a type of logistics ground support equipment (such as a cargo tractorand associated dollies loaded with cargo/packages) that is improved tobetter improvement performance during logistics operations and, in someembodiments, enhanced to avoid collisions with refined automated andautonomous and/or semi-autonomous features.

As noted above, the exemplary embodiments disclosed herein may be usedindependently from one another and/or in combination with one anotherand may have applications to devices, apparatus, systems and methods notdisclosed herein. Further, those skilled in the art will appreciate thatembodiments may provide one or more advantages, and not all embodimentsnecessarily provide all or more than one particular advantage as setforth here. Additionally, it will be apparent to those skilled in theart that various modifications and variations can be made to thestructures and methodologies described herein. Thus, it should beunderstood that the invention is not limited to the subject matterdiscussed in the description and, instead, is intended to covermodifications and variations using one or more of the aspects disclosedherein.

What is claimed:
 1. A retrofit assembly apparatus for use on a logisticsground support equipment to enhance a level of autonomous operation ofthe logistics ground support equipment, the assembly comprising: a refitcontrol system attached to the logistics ground support equipment, therefit control system comprising a vehicle dynamics control processor anda data preprocessing control processor; a plurality of retrofittedsensors coupled to the refit control system and disposed on thelogistics ground support equipment as retrofitted equipment to thelogistics ground support equipment, the retrofitted sensors comprising afirst group of proprioceptive sensors coupled to the vehicle dynamicscontrol processor that monitor operating parameters and characteristicsof the logistics ground support equipment, and a second group ofexteroceptive sensors coupled to the data preprocessing controlprocessor that monitors an exterior environment of the logistics groundsupport equipment; a plurality of actuators coupled to the refit controlsystem, wherein each of the actuators being disposed on the logisticsground support equipment as a retrofit actuator to add control ofdifferent ones of a plurality of control elements on the logisticsground support equipment and to autonomously alter motion of thelogistics ground support equipment; and wherein the refit control systemis programmatically configured to be operative to receive first sensordata generated by the first group of proprioceptive sensors using thevehicle dynamics control processor of the refit control system, receivesecond sensor data generated by the second group of exteroceptivesensors using the data preprocessing control processor, optimize a pathfor the logistics ground support equipment based upon the second sensordata from the data preprocessing control processor and the first sensordata from the vehicle dynamics control processor, and activate, by thevehicle dynamics control processor, at least one of the actuatorsaccording to the optimized path for the logistics ground supportequipment.
 2. The retrofit assembly apparatus of claim 1, wherein therefit control system is programmatically configured to be furtheroperative to: generate depth and spatial awareness information using thedata preprocessing control processor from the second sensor data, thedepth and spatial awareness information being related to the currentstate of the logistics ground support equipment; and wherein the refitcontrol system is programmatically configured to be operative tooptimize the path for the logistics ground support equipment based uponthe depth and spatial awareness information from the data preprocessingcontrol processor and the first sensor data from the vehicle dynamicscontrol processor.
 3. The retrofit assembly apparatus of claim 2,wherein the data preprocessing control processor is programmaticallyconfigured to eliminate the discrepant data from the second sensor databy being further operative to transform the second sensor data toextract a set of predetermined attributes related to autonomous movementfrom the discrepant data.
 4. The retrofit assembly apparatus of claim 2,wherein the data preprocessing control processor is programmaticallyconfigured to generate the depth and spatial awareness informationrelative to the current state of the logistics ground support equipmentby being further operative to receive the second sensor data over aperiod of time; identify a pattern from the received second sensor data,wherein the identified pattern is associated with one of a plurality ofenvironmental objects external to the logistics ground supportequipment, the identified pattern associated with the one of theenvironmental objects being at least part of the depth and spatialawareness information; identify at least a second of the environmentalobjects related to a current location of the logistics ground supportequipment as determined from location data in the received second sensordata, the identified at least second of the environmental objects beingat least part of the depth and spatial awareness information; andidentify at least a third of the environmental objects from one or moreexteroceptive sensor data types in the received second sensor data, theidentified at least third of the environmental objects being at leastpart of the depth and spatial awareness information.
 5. The retrofitassembly apparatus of claim 4, wherein the one or more exteroceptivesensor data types in the second sensor data comprises at least one fromthe group consisting of visual image data representing a predeterminedarea proximate the logistics ground support equipment, radar datarepresenting a predetermined area proximate the logistics ground supportequipment, multi-dimensional map data representing a predetermined areaproximate the logistics ground support equipment, and proximity datarepresenting a distance between the logistics ground support equipmentand one or more of the environmental objects.
 6. The retrofit assemblyapparatus of claim 4, wherein the environmental objects comprise atleast one from the group consisting of a lane marking, a road sign, avehicle disposed external to the logistics ground support equipment, anaircraft, a pedestrian, a signal light, a container, and a facilitywall.
 7. The retrofit assembly apparatus of claim 2, wherein the refitcontrol system is programmatically configured to be operative tooptimize the path for the logistics ground support equipment based uponthe depth and spatial awareness information from the data preprocessingcontrol processor and the first sensor data from the vehicle dynamicscontrol processor by being further operative to identify one of aplurality of control solutions for the logistics ground supportequipment that autonomously alters motion of the logistics groundsupport equipment with minimal limiting of speed of the logistics groundsupport equipment while avoiding impact of the logistics ground supportequipment with an environmental object identified from the depth andspatial awareness information.
 8. The retrofit assembly apparatus ofclaim 7, wherein vehicle dynamics control processor is further operativeto generate the plurality of control solutions as predicted states ofthe logistics ground support equipment based upon different settings forthe control elements on the logistics ground support equipment over aprediction horizon; evaluate each of the control solutions based upon acost function related to a performance characteristic of the logisticsground support equipment; and identify the one of the control solutionsbased upon the cost function.
 9. The retrofit assembly apparatus ofclaim 1, wherein the vehicle dynamics control processor is operative toactivate the at least one of the actuators according to the optimizedpath for the logistics ground support equipment to cause at least one ofthe control elements on the logistics ground support equipment to alterthe motion of the logistics ground support equipment.
 10. The retrofitassembly apparatus of claim 1, wherein the vehicle dynamics controlprocessor is operative to activate a plurality of the actuatorsaccording to the optimized path for the logistics ground supportequipment to cause at least two of the control elements on the logisticsground support equipment to change settings on the at least two of thecontrol elements resulting in a change in the motion of the logisticsground support equipment.
 11. The retrofit assembly apparatus of claim1, wherein the vehicle dynamics control processor is operative toactivate the at least one of the actuators according to the optimizedpath for the logistics ground support equipment to cause at least one ofthe control elements on the logistics ground support equipment to meet avector requirement defined by a determined heading and speed associatedwith the optimized path.
 12. The retrofit assembly apparatus of claim 1,wherein the first group of proprioceptive sensors includes a wheel speedsensor that generates at least a portion of the first sensor data basedupon monitoring a rotational speed at which a wheel on the logisticsground support equipment moves relative to the logistics ground supportequipment.
 13. The retrofit assembly apparatus of claim 1, wherein thefirst group of proprioceptive sensors includes a steering angle sensorthat generates at least a portion of the first sensor data based uponmonitoring an angle at which a steering wheel disposed on the logisticsground support equipment has been turned.
 14. The retrofit assemblyapparatus of claim 1, wherein the first group of proprioceptive sensorsincludes an engine run status sensor that generates at least a portionof the first sensor data based upon monitoring whether an engine on thelogistics ground support equipment is running.
 15. The retrofit assemblyapparatus of claim 1, wherein the first group of proprioceptive sensorsincludes a throttle position sensor that generates at least a portion ofthe first sensor data based upon monitoring a position of a throttledisposed on the logistics ground support equipment and coupled to anengine on the logistics ground support equipment.
 16. The retrofitassembly apparatus of claim 1, wherein the first group of proprioceptivesensors includes a brake status sensor that generates at least a portionof the first sensor data based upon monitoring motion of a brake pedaldisposed on the logistics ground support equipment.
 17. The retrofitassembly apparatus of claim 1, wherein the first group of proprioceptivesensors includes an engine revolution sensor that generates at least aportion of the first sensor data based upon monitoring enginerevolutions of an engine disposed on the logistics ground supportequipment.
 18. The retrofit assembly apparatus of claim 1, wherein thefirst group of proprioceptive sensors includes a driver present sensorthat generates at least a portion of the first sensor data based uponmonitoring at least one of a driver's presence on the logistics groundsupport equipment, the driver's ingress into the logistics groundsupport equipment, and the driver's egress from the logistics groundsupport equipment.
 19. The retrofit assembly apparatus of claim 1,wherein the first group of proprioceptive sensors includes a fuel levelsensor that generates at least a portion of the first sensor data basedupon monitoring a level of fuel disposed on the logistics ground supportequipment.
 20. The retrofit assembly apparatus of claim 1, wherein thefirst group of proprioceptive sensors includes a transmission statussensor that generates at least a portion of the first sensor data basedupon monitoring a status of a transmission system disposed on thelogistics ground support equipment.
 21. The retrofit assembly apparatusof claim 1, wherein the second group of exteroceptive sensors includesat least one camera sensor that generates at least a portion of thesecond sensor data as image data representing a predetermined areaproximate the logistics ground support equipment.
 22. The retrofitassembly apparatus of claim 1, wherein the second group of exteroceptivesensors includes at least one radar sensor that generates at least aportion of the second sensor data as radar data representing apredetermined area proximate the logistics ground support equipment. 23.The retrofit assembly apparatus of claim 1, wherein the second group ofexteroceptive sensors includes a first radar sensor that generates atleast a first portion of the second sensor data as forward radar datarepresenting a first predetermined area in front of the logistics groundsupport equipment; and a second radar sensor that generates at least asecond portion of the second sensor data as rear radar data representinga second predetermined area in back of the logistics ground supportequipment.
 24. The retrofit assembly apparatus of claim 1, wherein thesecond group of exteroceptive sensors includes a LiDAR sensor thatgenerates at least a portion of the second sensor data asmulti-dimensional map data representing a predetermined area proximatethe logistics ground support equipment.
 25. The retrofit assemblyapparatus of claim 1, wherein the second group of exteroceptive sensorsincludes a location sensor that generates at least a portion of thesecond sensor data as location data representing a current location ofthe logistics ground support equipment.
 26. The retrofit assemblyapparatus of claim 1, wherein the second group of exteroceptive sensorsincludes an ultrasound sensor that generates at least a portion of thesecond sensor data as proximity data representing a distance between thelogistics ground support equipment and an external object.
 27. Theretrofit assembly apparatus of claim 1, wherein a first of the actuatorscomprises a throttle actuator responsively controlling a throttledisposed on the logistics ground support equipment as one of the controlelements on the logistics ground support equipment, wherein responsivelycontrolling the throttle using the throttle actuator causes a change inspeed of the logistics ground support equipment according to theoptimized path.
 28. The retrofit assembly apparatus of claim 1, whereina second of the actuators comprises a brake actuator responsivelycontrolling a brake disposed on the logistics ground support equipmentas one of the control elements on the logistics ground supportequipment, wherein responsively controlling the brake using the brakeactuator causes the brake to decrease the motion of the logistics groundsupport equipment according to the optimized path.
 29. The retrofitassembly apparatus of claim 1, wherein a third of the actuatorscomprises a transmission actuator responsively controlling atransmission system disposed on the logistics ground support equipmentas one of the control elements on the logistics ground supportequipment, wherein responsively controlling the transmission systemusing the transmission actuator causes a change in gear selection forthe transmission system that alters the motion of the logistics groundsupport equipment according to the optimized path.
 30. The retrofitassembly apparatus of claim 1, wherein a fourth of the actuatorscomprises a steering actuator responsively controlling a steering systemdisposed on the logistics ground support equipment as one of the controlelements on the logistics ground support equipment, wherein responsivelycontrolling the steering system using the steering actuator causes achange in position for a steering wheel of the steering system thatalters a direction of movement for the logistics ground supportequipment according to the optimized path.
 31. The retrofit assemblyapparatus of claim 1, wherein a fourth of the actuators comprises anengine actuator responsively activate an engine disposed on thelogistics ground support equipment as one of the control elements on thelogistics ground support equipment, wherein responsively activating theengine starts the engine to allow for movement for the logistics groundsupport equipment according to the optimized path.
 32. The retrofitassembly apparatus of claim 1, wherein a fourth of the actuatorscomprises an engine actuator responsively deactivate an engine disposedon the logistics ground support equipment as one of the control elementson the logistics ground support equipment, wherein responsivelydeactivating the engine turns off the engine to prevent further movementfor the logistics ground support equipment according to the optimizedpath.
 33. The retrofit assembly apparatus of claim 1 further comprisinga feedback user interface coupled to the refit control system, thefeedback user interface being responsive to notification input generatedby the refit control system and being operative to generate operatorfeedback information for an operator of the logistics ground supportequipment, wherein the notification input generated by the refit controlsystem is triggered when the refit control system activates the at leastone of the actuators according to the optimized path for the logisticsground support equipment.
 34. The retrofit assembly apparatus of claim33, wherein the operator feedback information comprises an indicationthat the logistics ground support equipment is autonomously slowingdown.
 35. The retrofit assembly apparatus of claim 33, wherein theoperator feedback information comprises an indication that a steeringwheel on the logistics ground support equipment as one of the controlelements has been autonomously locked from manual control.
 36. Theretrofit assembly apparatus of claim 33, wherein the operator feedbackinformation comprises a visual indication that a state of the logisticsground support equipment has autonomously changed.
 37. The retrofitassembly apparatus of claim 33, wherein the operator feedbackinformation comprises an audible indication that a state of thelogistics ground support equipment has autonomously changed.
 38. Theretrofit assembly apparatus of claim 1 further comprising an operatordetection sensor as one of the first group of proprioceptive sensors;and wherein the refit control system is programmatically configured toactivate the at least one of the actuators from a limited subset of theactuators based upon detection data generated by the operator detectionsensor as part of the first sensor data.
 39. The retrofit assemblyapparatus of claim 1 further comprising an operator detection sensor asone of the first group of proprioceptive sensors; and wherein the refitcontrol system is programmatically configured to prevent activation ofthe at least one of the actuators based upon detection data generated bythe operator detection sensor as part of the first sensor data.
 40. Theretrofit assembly apparatus of claim 1, wherein the optimized pathcomprises an altered steering direction of the logistics ground supportequipment.
 41. The retrofit assembly apparatus of claim 1, wherein theoptimized path comprises an altered transmission setting for thelogistics ground support equipment.
 42. The retrofit assembly apparatusof claim 1, wherein the optimized path comprises an altered speed forthe logistics ground support equipment.
 43. The retrofit assemblyapparatus of claim 42, wherein the optimized path comprises a reducedspeed for the logistics ground support equipment.
 44. The retrofitassembly apparatus of claim 42, wherein the optimized path comprises acessation of movement for the logistics ground support equipment. 45.The retrofit assembly apparatus of claim 1, wherein the optimized pathcomprises a determined heading for moving the logistics ground supportequipment.
 46. The retrofit assembly apparatus of claim 1, wherein theoptimized path comprises an altered heading for moving the logisticsground support equipment and an altered speed for the logistics groundsupport equipment.